Investigating Interdisciplinary Collaboration: Theory and Practice across Disciplines 9780813585918

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Investigating Interdisciplinary Collaboration

The American Campus Harold S. Wechsler, Series Editor The books in the American Campus series explore recent developments and public policy issues in higher education in the United States. Topics of interest include access to college, and college affordability; college retention; tenure and academic freedom; campus labor; the expansion and evolution of administrative posts and salaries; the crisis in the humanities and the arts; the corporate university and for-­profit colleges; online education; controversy in sport programs; and gender, ethnic, racial, religious, and class dynamics and diversity. Books feature scholarship from a variety of disciplines in the humanities and social sciences. Gordon Hutner and Feisal G. Mohamed, eds., A New Deal for the Humanities: Liberal Arts and the Future of Public Higher Education Adrianna Kezar and Daniel Maxey, eds., Envisioning the Faculty for the Twenty-­First Century: Moving to a Mission-­Oriented and Learner-­Centered Model Scott Frickel, Mathieu Albert, and Barbara Prainsack, eds., Investigating Interdisciplinary Collaboration: Theory and Practice across Disciplines

Investigating interdisciplinary Collaboration Theory and Practice across Disciplines

Edited by

S c ot t F r i c k e l , M at h i e u A l b e r t, and Barbara Prainsack p r o lo g u e b y H e lg a N o w ot n y

R u tg e r s U n i v e r s i t y P r e s s New Brunswick, New Jersey, and London

This publication was supported in part by the Eleanor J. and Jason F. Dreibelbis Fund. Library of Congress Cataloging-­in-­Publication Data Names: Frickel, Scott, editor. | Albert, Mathieu, 1959-­editor. | Prainsack, Barbara, editor. Title: Investigating interdisciplinary collaboration : theory and practice across disciplines / edited by Scott Frickel, Mathieu Albert, and Barbara Prainsack ; foreword by Helga Nowotny. Description: New Brunswick, New Jersey : Rutgers University Press, 2016. | Series: The American campus | Includes bibliographical references and index. Identifiers: LCCN 2016008282| ISBN 9780813585895 (hardcover : alk. paper) | ISBN 9780813585888 (pbk. : alk. paper) | ISBN 9780813585901 (e-­book (epub)) | ISBN 9780813585918 (e-­book (web pdf)) Subjects: LCSH: Interdisciplinary approach to knowledge. | Interdisciplinary approach in education. | Interdisciplinary research. Classification: LCC BD255 .I65 2016 | DDC 001-­-­dc23 LC record available at https://lccn.loc.gov/2016008282 A British Cataloging-­in-­Publication record for this book is available from the British Library. This collection copyright © 2017 by Rutgers, The State University Individual chapters copyright © 2017 in the names of their authors All rights reserved No part of this book may be reproduced or utilized in any form or by any means, electronic or mechanical, or by any information storage and retrieval system, without written permission from the publisher. Please contact Rutgers University Press, 106 Somerset Street, New Brunswick, NJ 08901. The only exception to this prohibition is “fair use” as defined by U.S. copyright law. Visit our website: http://rutgerspress.rutgers.edu Manufactured in the United States of America

Contents

Acknowledgments vii

Prologue: The Messiness of Real-­World Solutions Helga Nowotny

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Introduction: Investigating Interdisciplinarities Scott Frickel, Mathieu Albert, and Barbara Prainsack

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pa r t i : i n t e r d i s c i p l i n a r y c u lt u r e s a n d c a r e e r s

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New Directions, New Challenges: Trials and Tribulations of Interdisciplinary Research David McBee and Erin Leahey The Frictions of Interdisciplinarity: The Case of the Wisconsin Institutes for Discovery Gregory J. Downey, Noah Weeth Feinstein, Daniel Lee Kleinman, Sigrid Peterson, and Chisato Fukuda Epistemic Cultures of Collaboration: Coherence and Ambiguity in Interdisciplinarity Laurel Smith-­D oerr , Jennifer Croissant, Itai Vardi, and Timothy Sacco Interdisciplinary Fantasy: Social Scientists and Humanities Scholars Working in Faculties of Medicine Mathieu Albert, Elise Paradis, and Ayelet Kuper

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pa r t i i : d i s c i p l i n e s a n d i n t e r d i s c i p l i n a r i t y

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Some Dark Sides of Interdisciplinarity: The Case of Behavior Genetics Aaron Panofsky

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vi Contents

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A Dynamic, Multidimensional Approach to Knowledge Production Ryan Light and jimi adams

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Disciplinary and Interdisciplinary Change in Six Social Sciences: A Longitudinal Comparison Scott Frickel and Ali O. Ilhan

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pa r t i i i : c h a n g i n g c o n t e x t o f interdisciplinary research

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“An Electro-­Historical Focus with Real Interdisciplinary Appeal”: Interdisciplinarity at Vietnam-­Era Stanford Cyrus C. M. Mody Interdisciplinarity Reloaded? Drawing Lessons from “Citizen Science” Barbara Prainsack and Hauke Riesch

10 One Medicine? Advocating (Inter)disciplinarity at the Interfaces of Animal Health, Human Health, and the Environment Angela Cassidy

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Notes on Contributors 237 Index 243

Acknowledgments

This book emerged out of several years of observing, analyzing, and discussing the workings of interdisciplinarity in our own lives as well as the lives of our colleagues, friends, and students. For some of us, interdisciplinarity has changed the institutions we work in, and affected our own practices and priorities. For all three of us, the political dimensions of interdisciplinarity in particular have been an area that we have sought to explore further, and in collaboration with others who have different perspectives and experiences. This desire to “dig deeper” into what drives interdisciplinarity, and how it drives academic collaboration, led to the conception of this volume. We are grateful to all contributors to this volume for their enthusiastic and, in fact, very disciplined collaboration. We also thank David Tobe for help with the index and Joseph Dahm for fantastic copyediting. Finally, heartfelt thanks go to our editors at Rutgers University Press, Leslie Mitchner, Katie Keeran, and—­down the long home stretch—­Kimberly Guinta and Kristen Bonanno, for their support and guidance. Scott Frickel, Mathieu Albert, and Barbara Prainsack Providence, Toronto, and London

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Investigating Interdisciplinary Collaboration

prologue The Messiness of Real-World Solutions H e lg a N o w ot n y

In recent times hardly a concept has enjoyed so much popular consensus across a wide range of different funding agencies, university administrators, policy makers, politicians, and the media as the idea of interdisciplinarity in research. A vast literature exists that has delved into the various dimensions believed to contribute, if not to constitute, the elusive aim of bringing together the right kind of available scientific knowledge with the necessary and practical know-­how in view of solving a concrete problem. Exploring how interdisciplinary research can live up to this task covers an enormous variety of actual scientific and technological practices, operating in very different organizational and institutional contexts across a vast scientific landscape. It includes the epistemological dimension when subtle and intricate encounters occur between different disciplines. It does not overlook the temporal dimension in the description of the emergence of a new discipline out of seemingly nowhere, combining theoretical, instrumental, and methodological know-­how of existing disciplines or of merging subdisciplinary fields. Yet, when asking why, despite the richness of the existing literature, relatively little progress toward interdisciplinarity in research has actually been achieved, a sense of defiant disappointment sets in. Somewhat unusual, the focus of a major part of this literature is therefore devoted to examining failures. The more attention is given to obstacles and the barriers that appear to prevent interdisciplinary research to flourish, the more urgent a link is established between their description and a call for action. What is singled out may be peer review and the admitted difficulties in coping with the evaluation of interdisciplinary research projects or the real or alleged conservatism of discipline-­centered academic gatekeepers and institutions. It may be that those who want to promote interdisciplinarity grossly underestimate the time needed to find a common language and to provide other facilitating 1

2 prologue

conditions for cross-­disciplinary engagement. Whatever obstacles are identified, the link between them and the appeal for their removal are so striking that this has become an interesting phenomenon in itself. Maybe this blatant gap between ideal and practice, between the vision and belief that interdisciplinarity yields better results and the sobering inquiry into the obstacles that prevent it from happening, tells us something about the perceived disconnect between the often referred to “real-­world” problems and the capacity of science to respond in an adequate and expected way. The manifold, messy, and complex problems of society for which we turn toward science and technology to provide solutions obviously do not translate easily into the kind of problem solving that drives scientific activity. If such a diagnosis is correct, the call for interdisciplinarity in research would function as a placeholder of an ideal, although highly simplified, vision of the relationship between “science” and “society.” The widespread imaginary of interdisciplinarity as producing better science and better solutions for society would be nothing but a proxy object—­unattainable and elusive, yet persistent as long as the aspirations, dreams, and misunderstandings that underpin it are not analyzed, named, and rendered visible. We might have to concede that the unbroken faith into more interdisciplinary research is nothing but a well-­intended, if desperate, call for action: to open up “science” for the “real” needs and problems that “societies” face. As such, it has preceded and continues to exist alongside other attempts that point in the same direction. Among them are more recent programs and manifestos like Responsible Research and Innovation and movements like Open Access and citizen science. But one can also take a more sober view of the same phenomenon. Funding agencies, acting on behalf of governments as the ultimate political authority to allocate taxpayers’ money, see it as their mission and responsibility to make sure that the research they fund will actually deliver results for society that can be measured and be accounted for. The pressure toward accountability and what is now called societal impact has increased considerably. Ex-­ante and ex-­post assessments have become more refined with greater reliance on metrics and other quantitative indicators. Seen from the perspective of funding agencies, administrators, and policy makers, the likelihood of increasing the return from research with high benefits for society—­which often translates into academic research contributing faster and with more direct impact toward innovation and economic growth—­is greater when scientific knowledge and know-­how are being pooled. “Only connect” is a seductive formula also for facilitating more collaboration across the implicated disciplines and institutions and, more generally, between academia, industry, and business. None of this can be accomplished through working within disciplinary boundaries only. The reaction as well as anticipation on behalf of researchers and the academic community is at least threefold. Partly, the demands of funding agencies are in

Prologue 3

resonance with their own experience. Researchers know very well that novel approaches, often introduced through new research technologies and instrumentation coming directly out of the lab, can transform a research field by opening up completely new vistas and opportunities. They are aware that innovative ideas in research often occur at the interface of disciplinary or subdisciplinary paradigms and approaches. They highly value the role played by serendipity in research, the unexpected discovery of new phenomena or connections one was not looking for and yet realizes their significance. It is in the very nature of serendipity that it is not bound to disciplines nor does it respect other institutional and circumstantial constraints. Experience tells the scientific community that the likelihood for any of these boosts in creativity to happen often increases by talking to people outside their own area and specialty. At the same time, they know that this is only the beginning. A long and arduous road lies ahead, and no certainty is ever guaranteed for the outcome. Then, there is the skepticism among researchers against certain forms of interdisciplinarity, equally rooted in experience. They are intimately aware of the difficulties of assessing the scientific quality of research projects that claim to be interdisciplinary. After all, peer review, with all the admitted weaknesses and faults, remains the daily life blood of doing science. They know about the widespread feeling among their peers that interdisciplinary research is often looked down upon as somehow lower in quality. This may be nothing but prejudice, but as long as it persists, it makes them wary to guide their PhD students and postdocs toward career paths where they are likely to encounter such prejudice. Finally, there is the realistic assessment that science as a whole is moving in the direction of more and larger collaborations. Researchers know that the quantity of multiple-­authored scientific publications is on the rise and that it correlates with higher citations. The internationalization of science is also greatly favored by the recognition that many of the most urgent societal challenges, from climate change to the eradication of poverty, from effectively fighting new epidemics to continuing work on healthy aging, can be tackled only by multidisciplinary, multinational, and multifunded forms of collaboration. Yet, many questions remain of how to best organize such and other desirable as well as necessary forms of collaboration. Here, issues of what is genuine interdisciplinarity and what are multi-­, cross-­, trans-­, or other forms of collaboration and how to set up conditions that favor and facilitate them return with a high policy-­relevant urgency. It is precisely at this junction of very different strands of inquiry into interdisciplinary research where the value of a fresh, critical look enters. Trying to make sense of these issues by carefully analyzing interdisciplinary research with a distant, yet engaged approach is the goal that the contributors to this volume strive

4 prologue

to achieve. Coming mostly from a background in the social sciences and inspired by an STS (science and technology studies) approach, they are well equipped to cast the empirical net of inquiry across a wide range of interdisciplinary research. They are ultrasensitive to the differences in organizational and institutional context in which it is situated and well attuned to take a temporal, historical dimension on board. They can draw upon a rich tradition of social theory that allows them to delve into the manifold practices of interdisciplinary research. They are determined to scrutinize the different strategies and purposes—­including the deliberate instrumentalization of interdisciplinary research—­that practitioners, funders, and policy makers deploy. They are in a good position to carefully compare what otherwise remains fragmented. The volume has the potential of putting interdisciplinary research into a larger frame of the ongoing transformation of the scientific enterprise. These processes span the macro level in the form of the ongoing globalization of science with its trends toward more collaborative ventures, as well the inner dynamics of different fields operating at the micro level. This includes the social sciences and humanities. A larger picture allows scholars to focus on the emergence of novel forms of how science is organized and organizes itself under the growing pressure of delivering faster and more tangible results and benefits. The necessity of including the social sciences and humanities in ways that are still to be determined becomes clear if problems and challenges are to be tackled at the real-­ world scale with its inherent messiness. The time may have come for a more honest and critically sharpened view of interdisciplinary research, one that is better grounded in the continuously evolving relationship between science and society. If this can be achieved, policy makers, funding agencies, and governments may gain a better and more realistic understanding of what is actually at stake, while initiating another major step in the responsiveness of science toward societal needs and problems.

Introduc tion Investigating Interdisciplinarities S c ot t F r i c k e l , M at h i e u A l b e r t, and Barbara Prainsack

In the past decade, a resounding call for interdisciplinary research—­ understood as collaborations between researchers across academic disciplines—­ has risen from virtually every corner of the academy and beyond (e.g., Canadian Institutes of Health Research 2009; European Commission 2014; National Institutes of Health 2007; Harvard University 2006; National Academy of Sciences 2005). Echoing through university faculty and administrations, funding agencies, and policy domains, this call is grounded in the assumption that interdisciplinary research generates more nuanced and robust understandings of the social and natural world than knowledge emerging from within traditional disciplines, and that it will lead to more innovative or more holistic solutions to “real-­ world” problems (Hadorn et al. 2010; Klein 1990; National Science Foundation 2011). Programmatic statements such as these often cast interdisciplinarity as an antidote to the limitations of disciplinary knowledge and as a panacea for the myriad problems facing our societies and our planet. Such arguments are extended and reinforced by pressures and incentives originating within and outside the academic field (Albert and McGuire 2014). Political and economic interests in the promotion of interdisciplinary research and scholarship loom large. Governments are adopting pro-­interdisciplinary innovation policies to stimulate national economic competitiveness (Albert and Laberge 2007; Weingart 2000; Woelert and Millard 2013). Funding mechanisms are being restructured or newly created to encourage researchers from different fields to engage in problem-­solving research and to collaborate with nonacademic “stakeholders” from industry, government, and civil society (see European Commission 2014; National Institutes of Health 2007). 5

6 introduction

Topic-­centered programs, institutes, and schools are emerging, many with the aim to train “the next generation of highly qualified problem-­solvers” (University of Toronto 2015). Amid this flurry of institutional rebranding and reorganization, a rapidly growing body of academic scholarship implicitly or explicitly celebrates these developments (e.g., Frodeman et al. 2010; Klein 2010b; Öberg 2009; Repko 2012). We think that these celebratory accounts give insufficient analytical attention to the insistent and sustained push from administrators, policy makers, and funding agencies to engineer new research collaborations across disciplines. In our view, the stakes of these efforts to seed interdisciplinary research and teaching “from above” are sufficiently high to warrant a rigorous empirical examination of the academic and social value of interdisciplinarity. Yet to date, such an empirically informed case for interdisciplinarity—­one that can withstand serious empirical scrutiny from different theoretical vantage points—­has not been made. Investigating Interdisciplinary Collaboration is premised on what we see as a pressing need for more rigorous empirical analysis of political and institutional support for interdisciplinarity, and the effects that interdisciplinary collaborations have on researchers, students, organizations, and knowledge. The book arises from our shared concern that, to date, much of the extant work on interdisciplinarity misses or overlooks the complexity of interdisciplinary politics and the social heterogeneity of interdisciplinary practices. This literature is often guided by narrowly framed research questions pointed at the problem of how interdisciplinary research collaborations are accomplished. The welter of studies describing the communication challenges researchers encounter in the context of short-­term interdisciplinary collaborations or “team science” is just one example (Heemskerk et al. 2003; Stokols et al. 2008; Thompson 2009; Winowiecki et al. 2011; Callard and Fitzgerald 2015). While gaining leverage on how problem-­ focused collaborations overcome technical language barriers is important, its significance as a program of research is limited by the absence of a broader set of inquiries into the institutional conditions that influence success or failure of team science. What makes team science across disciplines desirable in the first place? What political, economic, and societal values underlie its promotion? Interdisciplinary collaborations occur not in a social vacuum but within institutional settings that shape relationships between researchers, disciplines, theory traditions, and methodologies. Understanding interdisciplinary collaboration thus requires moving beyond a narrowly focused interactionist perspective. Instead we need to be attentive to power relations and status hierarchies between disciplines and knowledge areas, expectations of funders and administrators, and struggles for scientific authority. Detailed analyses of the epistemic foundations, institutional moorings, policy impacts, and social costs



Introduction 7

and benefits of interdisciplinarity are the best way to make cogent assessments of the reorganization of academic knowledge currently underway. Such analyses are essential for understanding why there is so much pressure on research institutions and individual researchers to embrace interdisciplinarity, and to understand the broader and longer-­term implications that this pressure is having on research and education landscapes. One of the missions of this book, then, is to begin to provide a closer and much needed empirical assessment of interdisciplinary research as practiced in Britain, Canada, and the United States. The ten chapters in this volume present perspectives from anthropology, history, political science, science studies, and sociology. These perspectives also reflect the academic contexts and work environments of our authors, ranging from traditional disciplinary departments to various interdisciplinary programs, institutes, schools and faculties. As the Notes on Contributors section attests, many of us have appointments in, or affiliations with, more than one discipline. The nature of such jobs requires that we maintain dual positions in the academic field, with one foot planted in the bedrock of traditional disciplinary departments and the other foot treading across less stable interdisciplinary terrain. Thus we come to our investigations of interdisciplinary research as practitioners of the same. Our skepticism is, we argue, a healthy one in that it is born from lived experiences of developing interdisciplinary organizations, practices, and sensibilities. We think this is entirely appropriate to the task we have set for ourselves in the pages that follow. In the remainder of this introduction we sketch a critique of social scientific studies of interdisciplinary research. Our critique is informed, first, by consideration of some different ways to characterize the sprawling body of more than eighteen hundred books, chapters, articles, essays, editorials, and reviews that emerge from a title search for “interdisciplinarity” in Google Scholar that we conducted at the time of this writing. Much of this literature advocates for interdisciplinary research; it also tends toward ideological treatment of the subject. Neither is particularly helpful in understanding what interdisciplinarity is or why we need it. Subsequently we will unpack three basic assumptions that underlie the slimmer body of work on interdisciplinarity emanating from the social sciences. These assumptions are, first, that interdisciplinary knowledge is better than disciplinary knowledge; second, that disciplines are silos that constrain the free development of interdisciplinary knowledge; and third, that interdisciplinary interactions are unconstrained by the status hierarchies and power asymmetries that operate within disciplines. Our concern with these assumptions is not that they are necessarily wrong, but that researchers too often take them for granted. This, we believe, has impeded systematic critical reflection and close empirical examination of the political values underpinning calls for interdisciplinarity.

8 introduction

Our position on these issues has important implications. Since the benefit of interdisciplinarity is also taken for granted in policy circles, decisions encouraging interdisciplinarity are often made based on faith more than on evidence. This has created a situation where funding bodies and other institutions have committed to an ideal that has various unintended consequences, such as negative effects on careers of interdisciplinary scholars, or the longer term effects of the interdisciplinary reorganization of research institutions and universities (see, e.g., chapters in this volume by Albert et al., McBee and Leahey, and Mody). By critically addressing these and other gaps in the current understanding, our hope is that this book will provide the kind of systematic social knowledge policy makers need to craft effective interdisciplinary research and education policy. Contributors to this volume begin this work by making one or more of these assumptions explicit.

Mapping Interdisciplinary Landscapes The body of scholarly writing that covers interdisciplinary research is vast. Developed by researchers from dozens of fields and subfields, this sprawling literature addresses a wide range of topics from various perspectives; it is written for different purposes and for diverse audiences. Rather than attempt a comprehensive summary of this material, it may be more helpful to consider some ways to more selectively map different features of this large and complex body of work. Ecologies of Interdisciplinary Knowledge

One way to organize this material is to map interdisciplinarity through the lens of an “ecology of knowledge” (Rosenberg 1979). This approach would aim to identify interrelationships considered constitutive of interdisciplinary work in particular fields or broader institutional domains. Doing so is relatively straightforward because so many people who write about interdisciplinarity do so from within the context of one or another discipline or area of research—­most often their own. As a result, it is possible to identify concentrated “pockets” of scholarly writing and research on, say, interdisciplinarity and its impacts in education (Davies et al. 2010; Hall and Weaver 2001; Lattuca 2001), sociology (Calhoun and Rhoten 2010; Christie and Maton 2011), or environmental studies (Lélé and Norgaard 2005; Phillipson et al. 2009; Strang 2009). Other scholars have taken on larger swaths of academic terrain, writing on interdisciplinarity across broad institutional domains such as the natural and social sciences (Barry and Born 2013; MacMynowski 2007; Callard and Fitzgerald 2015), the humanities (Klein 2005), or higher education studies (Brint 2005; Brint et al. 2009; Graff 2015). Research problems attract similar levels of concentrated writing and research



Introduction 9

activity. Thus we find interdisciplinary perspectives on issues of widespread social concern such as climate change (Bhaskar et al. 2010), social justice (Parker et al. 2010), or medical education (Hodges and Lingard 2013). Mapping the ecological relations among different pockets of scholarly writing and research about interdisciplinary research can be a valuable way to proceed. For example, this approach could tell us a great deal about the distribution of academic interest in interdisciplinarity as a topic of study. It could also tell us something about how interdisciplinary practices might vary from one domain to the next or about how different domain-­based practices are changing in relation to one another. Phases and Aspects of Interdisciplinary Knowledge Creation

A second strategy for mapping the field is to focus on substantive areas of topical research that fall within the broad scope of “interdisciplinarity.” When academics conduct research on interdisciplinarity, what specifically do they study? While there are many ways to measure topical popularity or attention, most “top ten” lists would likely include studies of the epistemological status of interdisciplinary knowledge (e.g., Fuller 2004; Froderman 2014; Weingart 2000); structural and cultural barriers to interdisciplinary programs, research, and careers (e.g., Rhoten and Pfirman 2006; Miller et al. 2008; Sá 2007; Jacob and Prainsack 2010; Prainsack et al. 2010); interdisciplinary research activities (Albert et al. 2015; Dewulf et al. 2007; Rafols 2007); emergent standards of evaluation (e.g., Albert et al. this volume; Huutoniemi 2012; Laudel 2001; Laudel and Origgi 2006; Mallard et al. 2009; Lamont et al. 2006; Lamont 2009); and the emergence and institutionalization of interdisciplinary fields (e.g., Frickel 2004b; Panofsky 2011; Rojas 2007). This approach can provide a studied sense of what is known about interdisciplinary research, broadly construed. With such a map, we could begin to understand the extent of the heterogeneity of epistemic cultures contributing to interdisciplinary knowledge and how these are multiplied or refracted through interdisciplinary settings. This approach might also tell us something about the politics of interdisciplinarity studies to the extent that a map of interdisciplinary research topics encourages critical examination of their uneven distribution or why some topics receive less attention than others. Finding and Mining the Gaps

A third approach to categorizing the literature is to examine research in relation to the untested or taken-­for-­granted assumptions that underlie knowledge claims and policy recommendations pertaining to interdisciplinarity ( Jacobs

10 introduction

and Frickel 2009). This is the mapping approach we pursue in more detail in the following section. We do so because it allows us to find and mine extant knowledge gaps—­epistemic absences that unnecessarily constrain how we think about, study, and conduct interdisciplinary research. From a practicality standpoint, an examination of underlying assumptions can help make visible the epistemic machinery (Knorr Cetina 1999) of interdisciplinarity studies. It helps us identify conceptual ambiguities that can impede understanding and theory development—­the frequent conflation of interdisciplinarity with “applied research” is just one example. It can also shed light on the field’s commitments to different methods and tools of analysis. In short, making visible the unwritten assumptions built into a field of research can provide important insight into the kinds of knowledge fields do and do not produce. From a politics of knowledge standpoint, consideration of a field’s underlying assumptions can help us better understand how ideas about interdisciplinarity emerging from extant studies are used to advance various intellectual and political projects and subvert others (Frickel and Gross 2005). Calls for interdisciplinary research in the academy are not a novelty of the past decades (Abbott 2001), but the increased scale and prominence of such calls today certainly invites sustained and systematic attention to the politics of interdisciplinarity as an important development in contemporary intellectual movements. What explains the current institutional and political appeal of interdisciplinarity? What is the reason for the unrivaled popularity of interdisciplinary discourse and policy? Is the emergence of this intellectual movement connected historically to other political and social movements? What do top-­down interdisciplinary policies tell us about the maintenance or loss of academic autonomy in the face of mounting political and market pressures? We argue that important aspects of dominant understandings and prescriptions of how interdisciplinary research should be done, what purposes it should serve, and why researchers should get involved with it are rooted in three important assumptions that underlie calls and incentives for interdisciplinary research (and remain unexamined in much of the scholarship on interdisciplinarity). To these we turn next.

Assumptions about Interdisciplinary Research In this section we describe three interrelated assumptions that are implicit in the literature on interdisciplinarity. Each of these assumptions plays a significant role in justifying the creation of interdisciplinary knowledge, research centers, and policies, yet each also remains understudied. These lacunae we believe limit the authority of interdisciplinarity as an intellectual movement and may endanger the academic and scientific institutions the movement claims it can strengthen.



Introduction 11

Interdisciplinary Knowledge Is Better Knowledge

Sifting through the body of work on interdisciplinarity one is likely to encounter the idea that interdisciplinary research leads to knowledge that is superior to knowledge identified as not interdisciplinary. Indeed, this assumption is widespread and rarely contested (e.g., see National Academy of Sciences 2005; Canadian Institutes of Health Research 2009; European Union 2010; Hall et al. 2006; Hadorn et al. 2010). Recently interdisciplinarity has also been promoted as a way to increase public accountability (Huutoniemi 2015), or seen to enhance the impact of academic work (Larivière et al. 2015). The latter is particularly interesting in light of the operationalization of “impact” as an assessment criterion in the national research evaluation framework in the United Kingdom, which influences the distribution of public funding to universities (Martin 2011; McKenna 2015). The presumed superiority of interdisciplinary knowledge also implicitly informs many efforts to distinguish what is epistemologically special about interdisciplinary research (Faber and Scheper 1997; Fuller 2004), efforts to develop taxonomies of interdisciplinary research (Aboelela et al. 2007; Huutoniemi et al. 2010; Klein 2010a; Salter and Hearn 1996), and work examining the everyday dynamics of interdisciplinary interactions in small-­group research settings (Hackett and Rhoten 2009; Jeffrey 2003; Scerri 2000; Stokols et al. 2003). On the face of it, the appeal of the idea that interdisciplinarity leads to superior knowledge seems compelling enough. After all, why else would funding agencies promote it and why would so many researchers undertake something so difficult and time-­consuming if interdisciplinary research did not promise benefits that more traditional disciplinary research cannot deliver? But it is not nearly that simple. A major problem that one confronts in assuming the superiority of interdisciplinary research is a basic lack of studies that use comparative designs to establish that measurable differences in fact exist and to demonstrate the value of interdisciplinarity relative to disciplinary research. Most comparative studies that we are familiar with compare practices in different interdisciplinary contexts, such as interdisciplinarity in the natural and social sciences (Barry and Born 2013); other studies compare the emergence of different interdisciplinary fields such as ethnic studies and women’s studies (Brint et al. 2009; Olzak and Kangas 2008). Such studies do not, however, compare disciplinary and interdisciplinary fields or knowledge creation activities with each other (a key exception is Jacobs 2013). Cognizant of this gap within existing research, Frickel and Ilhan (this volume) compare the growth of interdisciplinary social sciences relative to the growth of social science disciplines over the past three decades. Results show that as social science fields grow, individual units representing those fields on university and

12 introduction

college campuses tend to become less competitive locally, a finding that holds for disciplines and interdisciplines alike. The observation that in the social sciences, interdisciplines and disciplines are in some ways more similar than different points to a second major problem with the assumption that interdisciplinary research leads to superior outcomes. The problem derives from the fact that disciplines and interdisciplines are both dynamic knowledge forms whose boundaries and practices are continuously in flux. As such, drawing distinctions between interdisciplinary knowledge and its counterpart “other” can often be anything but clear-­cut. The potential for ambiguity is real and raises profound questions about appropriate units of analysis and scope conditions that scholars have yet to tackle empirically: How do we determine whether the knowledge generated from scientific activities is or is not interdisciplinary? Against what criteria are judgments between the value of interdisciplinary versus other kinds of knowledge to be made? Who makes these decisions, and whose interests do those decisions serve? From a sociohistorical perspective, differences between disciplinary and interdisciplinary knowledge are seldom self-­evident. Interdisciplinarity means different things to different people; it is generated through different practices in different areas of research; and it is used in different ways by different groups with different interests, goals, and expectations (see Smith-­Doerr et al. in this volume). This heterogeneity is reflected in the absence of a common definition of the concept (Callard and Fitzgerald 2015; Moore 2011; Rosenfield 1992). Moreover, research and scholarly inquiry are practices that are influenced by organizational, economic, and social factors and thus reflect the needs and stakes of each phase and place in history (see Downey et al., Smith-­Doerr et al., and Cassidy in this volume). What is considered a scientific discipline at any moment in time can depend, for example, on whether a field has its own canonical body of work that all novices have to be trained in before they are considered practitioners of this discipline. Whether or not such a canon exists often depends, in turn, on how socially and economically valuable the field is considered to be, or whether practitioners in the field organize themselves to create disciplinary identities (Crozier 2001; Clarke 1998). This shows that assumptions about categorizations being obvious or nonproblematic are themselves expressions of knowledge politics. We are reminded that the work of categorization is never objective or neutral, but always value laden and consequential (Albert et al. 2008, 2009; Bowker and Star 1999; Bourdieu 1984; Gieryn 1999). Among other things, the ambiguity inherent in our understandings of interdisciplinarity underscores the need for more reflexive, and more explicit, consideration of an author’s own positions and stakes in the knowledge politics that interdisciplinarity has come to represent.



Introduction 13

Several chapters in our volume examine various aspects of these questions. In her study of the emergence of the One Health movement, Angela Cassidy shows how the idea that our understanding of health should be extended to nonhuman animals and other species made research that crossed these boundaries seem superior to research that remained within the disciplinary boundaries of human health, veterinary medicine, or other disciplines. Barbara Prainsack and Hauke Riesch draw parallels between the hopes imbued in “citizen science” on the one hand with those associated with interdisciplinarity on the other; both are seen by their proponents as leading to “better” science, while the values and criteria underlying this assessment often remain implicit. Mathieu Albert et al. ask what kind of interdisciplinary research is created when a field is dominated by members of one scientific discipline from the outset. Disciplines Constrain Interdisciplinary Knowledge

A second assumption underlying much of the current work on interdisciplinarity is that disciplines operate as institutional silos that constrain interdisciplinary knowledge. This general assumption has informed many efforts to identify cultural and structural factors that limit interdisciplinary knowledge creation and use. As a result, examples of the constraining impacts of disciplines now include barriers to epistemic, technological, and language translation (Duncker 2001; Lélé and Norgaard 2005; Marzano et al. 2006; Reich and Reich 2006); institutional barriers such as departmental organization and reward structures (Laudel and Origgi 2006; Wallerstein 1996); hostile or undeveloped criteria for evaluating interdisciplinary research (Feller 2006; Laudel 2001; Mallard et al. 2009); and the undervaluation of interdisciplinary publications (Campbell 2005; Chubin et al. 1984). By contrast, we find far fewer efforts to understand whether and how interdisciplinary research may exert similar constraints on knowledge created in and flowing from disciplines (see Albert et al. 2015 and in this volume). Constraints to knowledge production are ubiquitous; whether and how they disproportionately impact one type of knowledge or another is an empirical question that awaits systematic investigation. Even so, embedded within disciplinary impact studies one is likely to find presumptive arguments relating to the centripetal nature of disciplinary activity. The common perception is that disciplines are inward-­looking or self-­referential research enterprises accountable only or primarily to themselves. Working within their own disciplines, the argument goes, researchers are more inclined to collaborate with one another than to team up with researchers from other knowledge domains. And, because disciplinary boundaries are policed from inside, they are relatively impermeable to outside ideas or to social accountability. In

14 i n t r o d u c t i o n

this way, disciplines are seen to constrain not only the production of interdisciplinary knowledge but also its circulation. Yet this subsidiary argument too is often based on anecdotal evidence and lacks strong empirical support. Recent bibliometric analyses conducted by Jerry Jacobs (2013; Jacobs and Frickel 2009) suggest that, at the very least, the assumption of impermeable disciplinary boundaries is potentially flawed: ideas, concepts, theories, and methodological tools seem to travel quite fluidly, even between far-­flung disciplines. Moreover, bibliometric studies also show that disciplines contribute lots of knowledge about issues of societal importance including AIDS, immigration, climate change, sexism, domestic violence, homelessness, and terrorism ( Jacobs 2013; see also Kellert 2008). As argued by Jacobs (2013), disciplines are neither the silos nor the ossified turf depicted by many interdisciplinary promoters and implied by many disciplinary impact studies. Instead, disciplines are dynamic and permeable institutions and can evolve through the work accomplished by scholars expanding their disciplinary proficiency (see chapters by Light and adams and by McBee and Leahey in this volume). Furthermore, disciplines constantly borrow from one another to foster “routine interdisciplinarity,” a term proposed by Rob Moore (2011) to describe the mundane yet continuous and consequential knowledge crisscross among disciplines. If disciplines are not silos, why is the silo rhetoric so pervasive? One likely reason, suggested earlier, may be that there has simply not been enough research conducted to date to adequately assess the accuracy of this common refrain. Few existing studies investigate the nature of boundaries and boundary maintenance in interdisciplinary contexts (Frickel 2004a; Panofsky, this volume). But another possible reason, and one that we think also deserves more attention than it has thus far received, involves strategic efforts to legitimate interdisciplinary research from above. While advocating for inclusiveness, national and institutional policies that support interdisciplinarity may ironically help produce the opposite by pushing disciplinary knowledge to the margins (especially knowledge from disciplines considered to be too theoretical, or “soft”). An emerging body of work from Canada suggests that interdisciplinary policies may not erase divisions between fields so much as redraw them in different places, for example, between research that is valued by policy makers and funding agencies and research that is not (Albert and Paradis 2014; Albert et al. 2015, and this volume; Katelin Albert 2014). Since the creation of the Canadian Institutes of Health Research in 2000, federally mandated interdisciplinarity has created new forms of insularity and exclusion. Health services research, epidemiology, and other fields of applied research have gained resources and institutional power while sociology, anthropology, and other less applied fields, including basic wet lab research, are often dismissed as too theoretical and too



Introduction 15

curiosity-­driven and therefore less useful to address concrete health-­related problems (see Graham et al. 2011 and Webster 2015 for incisive accounts of the impact of top-­down interdisciplinary policies on medical anthropology and basic science in Canada). By redefining what counts as legitimate research, top-­ down interdisciplinarity, in addition to—­and often instead of—­breaking down silos and eliminating orthodoxies, helps to establish new ones. Aaron Panofsky, in his contribution to this volume, examines aspects of the questions we raised here. Using behavior genetics as a case study, he argues that this particular interdisciplinary field suffers from many of the epistemic shortcomings supposedly endemic to disciplines: fragmented communication, repetitive knowledge production, and rigidity of certain epistemic assumptions held by researchers. Interdisciplinary Interactions Are Unconstrained by Hierarchies

The third assumption underlying much of the literature is that interdisciplinary interactions are unconstrained by disciplinary hierarchies and power asymmetries. In such settings, researchers with any academic background are seen as contributing equally to the research enterprise with none exerting predominance over the orientation of the research project (Armstrong 2006). At its core, the interdisciplinary vision assumes that the traditional social order in the scientific field, characterized by turf wars among the disciplines, would be replaced by new arrangements that are unconstrained by boundary work and disciplinary hierarchies. In line with this assumption, the current system of scientific knowledge production, unduly subjected to the dominance of disciplines, needs to be restructured in order to create the conditions for interdisciplinarity to flourish and allow a freer meeting of the minds (Hall et al. 2006). This assumption often manifests itself in the expectation that the weakening of disciplinary institutions will reduce disciplinary hierarchies and power asymmetries that isolate researchers in different fields from one another, give legitimacy to some research questions over others, and prioritize certain research techniques while others are marginalized. A corollary expectation is that when researchers from various disciplines do get together to tackle a specific problem or issue, or when they “borrow knowledge” (Kellert 2008) from one another, they do so as equal partners and stripped of their disciplinary prejudices and blinders (see Hall et al. 2006, for an example of this position). In this ideal world, past and current forms of science, dominated by struggles for scientific authority and resources, would be eclipsed by collegial cooperation between different epistemic cultures and academic professions. Such idyllic visions of interdisciplinary collaborations go beyond the mere promotion of a different way of knowledge creation; they envisage a new type of researcher, one that must acquire and demonstrate the proper dispositions to

16 introduction

become a good interdisciplinary citizen and ensure the success of the team. As stated in an instruction booklet for academics who are developing interdisciplinary research proposals in Scotland, useful personal characteristics for successful interdisciplinary work include “flexibility, adaptability, creativity” and “good communication and listening skills” (Tait and Lyall 2007, 3). But are these not characteristics that any academic aiming to do good research should have or seek to acquire? If so, what is to be gained by associating these characteristics narrowly with interdisciplinary research? As contributions to this volume show, hierarchies, prejudices, and power asymmetries continue to play an important role in interdisciplinary interactions (Albert et al.; McBee and Leahey; and Mody). So much so that interdisciplinary researchers who come from disciplines that are seen as “soft” or less prestigious often find themselves frustrated or boxed into corners within a new interdisciplinary order (see Albert et al. 2015; Prainsack et al. 2010; Callard and Fitzgerald 2015). Still another layer of complications arises when scholars who enter interdisciplinary collaborations early in their careers, and especially those having undergone graduate training in a “new” or interdisciplinary field, are often seen by older colleagues as lacking full canonical training in a solid discipline (Eisenberg and Pellmar 2000; Lynch and Cole 2005; and Downey et al. this volume).

Thematic Overview of the Book This book contributes to critical studies of interdisciplinarity by challenging the three unexamined assumptions discussed in the previous section: (1) interdisciplinary knowledge is better than disciplinary knowledge, (2) disciplines are silos that constrain the development of interdisciplinary knowledge, and (3) interdisciplinary interactions are unconstrained by the status hierarchies and power asymmetries that operate within disciplines. Contributors to this volume challenge these assumptions by looking at the way in which interdisciplinary research is envisaged, enacted, and reflected in the architecture of research institutions; by making visible the boundary work that goes into portraying the “other” of interdisciplinary research as homogenous, oppressive, and unable to transgress its own allegedly epistemic limitations; and by paying attention to the costs that interdisciplinarity has for many of the researchers “incentivized” to engage in it. The book is organized in three thematically oriented sections. The first focuses on culture and careers, the second targets the relationship between disciplines and interdisciplinarity, and the third places emphasis on the changing contexts in which interdisciplinary research is conducted. The four chapters grouped under the first theme show that interdisciplinarity is highly context-­dependent. From a definitional perspective, the chapters by



Introduction 17

Laurel Smith-­Doerr and her colleagues as well as the chapter by Greg Downey and his colleagues show that players in the scientific field hardly share a common understanding of interdisciplinarity; instead, in both chapters we meet individuals who deploy meanings of interdisciplinarity in strategic and often contradictory ways depending on the contexts in which they operate. These two chapters shed new light on how the semantic plasticity of the term “interdisciplinarity” allows it to operate as a “boundary object” that is invested with various meanings to achieve various goals. The two other chapters in this section examine the professional costs of interdisciplinarity. While each chapter has a different starting point, both expose a similar phenomenon underexamined by previous research on interdisciplinarity: the dynamics of boundary work within interdisciplinary communities. The chapter by David McBee and Erin Leahey considers the challenges faced by humanities scholars seeking to broaden the scope of their knowledge beyond their primary discipline. The chapter by Mathieu Albert and colleagues relates the experiences of social scientists and humanities scholars working in the interdisciplinary context of Canada’s medical research field. Taken as a whole, these four chapters raise pivotal questions that will merit further investigation beyond this volume: In what contexts and instances is interdisciplinarity used as a rhetorical device by stakeholders to grant legitimacy to agendas that have to do more with politics of knowledge than with making inroads to interstitial territories? Do top-­down interdisciplinary policies create new forms of disciplinary exclusion? If so, how can we explain the decoupling between the discourse of inclusiveness intrinsic to interdisciplinary policies and the exclusion of disciplinary researchers and their knowledge claims resulting from the implementation of such policies? The three chapters grouped under the second theme examine the complex relationship between disciplines and interdisciplinarity. Using the fields of religion, demography, and HIV/AIDS research as exemplars, Light and adams present a new model for understanding how research changes based on the characteristics of the networks and boundaries that separate or distinguish academic fields. Within their model, interdisciplinarity, disciplinarity, multidisciplinarity, and transdisciplinarity are conceptualized as particular states in the dynamic life of a research field. These different states hold implications for practitioners whose work is constrained by the characteristics of a particular state at a particular time. In his chapter on the field of behavior genetics, Aaron Panofsky shows how scientists used their membership in this interdisciplinary field to advance their career within the context of their own discipline and vice versa. As Panofsky reminds us, disciplines and interdisciplines are not two separate and closed epistemic spaces, but rather interconnected territories that scientists crisscross for various purposes, including enhancing their own symbolic status.

18 introduction

The third chapter in this section, by Scott Frickel and Ali Ilhan, offers a statistical analysis of the relationship between disciplines and interdisciplines. Explicitly comparative, their study investigates the organizational and social factors that have shaped patterns of change among three traditional social science disciplines and three social science interdisciplines over a thirty-­year period. Their study suggests that institution size accounts for the largest variation in temporal change among both types of fields, despite real and persistent organizational differences. Frickel and Ilhan’s findings add complexity to current debates about the nature and role of interdisciplinary organization of higher education and suggest important avenues for future research. The book’s third section also contains three chapters, each of which considers the changing contexts of interdisciplinary research. Cyrus Mody offers a fascinating account of how Stanford University sequentially encouraged different forms of interdisciplinarity as an evolving strategy for attracting resources from outside academia. As he cogently argues, the call by university administrators for different kinds of interdisciplinary research is best understood when seen as a response to changes in the broader institutional environment in which Stanford successively found itself. Barbara Prainsack and Hauke Riesch argue that citizen science, like interdisciplinarity, is supposed to disrupt the conduct of traditional science and lead academic knowledge in new and fruitful directions. They argue that there are intriguing parallels between interdisciplinarity and citizen science in the way that both are employed to provide a vision of what other, better science could and should be. Drawing upon scholarship in the sociology of expectations, they argue that these visions are “real” in that they shape our understanding of what valuable research is, and who we should collaborate with. The third section’s final chapter, by Angela Cassidy, takes a longitudinal perspective to tell a story about interdisciplinarity in which access to resources emerges as one of the primary motivations for pursuing institutional change. Using the One Health label, veterinarians in the United Kingdom advocated for closer collaborations with human medicine as a means to increase their professional status and access to resources. However, when the One Health agenda was taken up by powerful institutions such as the Centers for Disease Control in the United States and the World Health Organization, veterinarians lost control of the agenda and merged back into the biomedical sciences. In this case, changing context has meant losing the ground veterinarians had gained previously by positioning their profession as complementary to human medicine.



Introduction 19

Conclusion We conclude this introduction with an invitation to readers to investigate with us the social complexities and tensions shaping interdisciplinary collaboration. We hope that this volume helps to consolidate interdisciplinarity studies as an area that is characterized by rigorous empirical and theory-­driven scholarship. The field is not there yet, but we think the tide is turning and that a more critical and balanced assessment of the conditions that produce interdisciplinarity and its epistemic, social, and political consequences is not far off. We envisage this field, once developed, as one that unpacks previously held assumptions about the benefits or pitfalls of interdisciplinarity with the help of comparative empirical work and careful conceptualization that is sensitive to power asymmetries and to the stakes and experiences of researchers who work in both disciplinary and interdisciplinary contexts. Such work can and should lead to policies for interdisciplinary education and research that are grounded in evidence rather than resting upon unverified assumptions. Therein lies our best hope for developing a sufficiently deep understanding of the rapidly changing conditions of academic work. Note: The editors thank Tim Sacco for helpful comments on a previous version of this essay. references

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Introduction 21

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Introduction 23

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24 introduction Rojas, Fabio. 2007. From Black Power to Black Studies: How a Radical Social Movement Became an Academic Discipline. Baltimore: Johns Hopkins University Press. Rosenberg, Charles. 1979. “Toward an Ecology of Knowledge: On Discipline, Context, and History.” In No Other Gods: On Science and American Thought, edited by Alexandra Olesen and John Voss, 440–­455. Baltimore: Johns Hopkins University Press. Rosenfield, Patricia L. 1992. “The Potential of Transdisciplinary Research for Sustaining and Extending Linkages between the Health and Social Sciences.” Social Science & Medicine 35 (11): 1343–­1357. Sá, Creso. 2007. “Interdisciplinary Strategies in U.S. Research Universities.” Higher Education 55 (5): 537–­552. Salter, Liora, and Alison Hearn, eds. 1996. Outside the Lines: Issues in Interdisciplinary Research. Montreal: McGill-­Queens University Press. Scerri, Eric R. 2000. “Interdisciplinary Research at the Caltech Beckman Institute.” In Practicing Interdisciplinarity, edited by N. Stehr and P. Weingart, 194–­214. Toronto: University of Toronto Press. Stokols, Daniel, Juliana Fuqua, Jennifer Gress, Richard Harvey, Kimari Phillips, Lourdes Baezconde-­Garbanati, Jennifer Unger, Paula Palmer, Melissa A. Clark, Suzanne M. Colby, Glen Morgan, and William Trochim. 2003. “Evaluating Transdisciplinary Science.” Nicotine and Tobacco Research 5 (1S): S21–­S39. Stokols, Daniel, Kara L. Hall, Brandie K. Taylor, and Richard P. Moser. 2008. “The Science of Team Science: Overview of the Field and Introduction to the Supplement.” American Journal of Preventive Medicine 35 (2S): S77–­S89. Strang, Veronica. 2009. “Integrating the Social and the Natural Sciences in Environmental Research: A Discussion Paper.” Environment and Sustainable Development 11 (1): 1–­18. Tait, Joyce, and Catherine Lyall. 2007. “Short Guide to Developing Interdisciplinary Research Proposals.” Briefing document, ISSTI, University of Edinburgh. http://www.law.ed.ac. uk/__data/assets/file/0005/77603/ISSTI_Briefing_Note_1.pdf. Thompson, Jessica Leigh. 2009. “Building Collective Communication Competence in Interdisciplinary Research Teams.” Journal of Applied Communication Research 37 (3): 278–­297. University of Toronto. 2015. “Director’s Message.” Translational Research Program, Institute of Medical Science. http://trp.utoronto.ca/directors-­message/. Accessed July 21, 2015. Wallerstein, Immanuel. 1996. Open the Social Sciences: Report from the Gulbenkian Commission on the Restructuring the Social Sciences. Stanford, CA: Stanford University Press. Webster, Paul C. 2015. “CIHR’s Commitment to Basic Science Questioned.” Canadian Medical Association Journal 187 (7): 484. Weingart, Peter. 2000. “Interdisciplinarity: The Paradoxical Discourse.” In Practicing Interdisciplinarity, edited by N. Stehr and P. Weingart, 25–­41. Toronto: University of Toronto Press. Winowiecki, Leigh, Sean Smukler, Kenneth Shirley, Roseline Remans, Gretchen Peltier, Erin Lothes, Elisabeth King, Liza Comita, Sandra Baptista, and Leontine Alkema. 2011. “Tools for Enhancing Interdisciplinary Communication.” Sustainability: Science, Practice, & Policy 7 (1): 74–­80. Woelert, Peter, and Victoria Millar. 2013. “The ‘Paradox of Interdisciplinarity’ in Australian Research Governance.” Higher Education 66 (6): 755–­767.

1 ◆ New Direc tions, New Challenges Trials and Tribulations of Interdisciplinary Research D av i d M c B e e a n d E r i n L e a h e y

Interdisciplinary research (IDR), “a mode of research by teams or individuals that integrates information, data, techniques, tools, perspectives, concepts, and/or theories from two or more disciplines or bodies of specialized knowledge” (National Academy of Sciences 2005, 188), is viewed as beneficial to science and society (Rhoten and Parker 2004). This optimism has catalyzed efforts at various levels (national, state, organizational) to fund IDR projects and promote IDR centers. Indeed, Porter and Rafols (2009) document a small but steady rise in interdisciplinary scholarship, especially since the mid-­1980s. Interdisciplinarity is a timely and nationally important topic. Despite this growth and optimism, systematic investigations of the impact of interdisciplinarity on individual careers are rare ( Jacobs and Frickel 2009). Without strong empirical grounding, the widespread attention to—­and praise for—­interdisciplinarity may be premature. Few studies “seek to understand empirically the links between institutional initiatives, individual attributes, and professional implications” (Rhoten and Parker 2004, 2046). Recent exceptions include Albert and Paradis’s (2014) study of social scientists and humanists employed in medical schools, Frost and Jean’s (2003) evaluation of an initiative at Emory University, Lattuca’s (2001) qualitative study of interdisciplinary faculty, and Leahey et al.’s (2015) quantitative analysis of IDR’s costs and benefits. These studies suggest that while engaging in IDR can be rewarding, it can also be challenging. We suspect that IDR is difficult and time-­consuming to produce; this, combined with reviewers’ difficulties evaluating IDR (Lamont et al. 2006), 27

28 interdisciplinary cultures and careers

may thus dampen productivity. Interdisciplinary scholars may lack the support needed to work through these challenges, leading to feelings of dismay or regret. In this chapter we empirically explore and elaborate upon the professional risks and challenges of interdisciplinary scholarship and training. We do this by interviewing tenured interdisciplinary scholars in the humanities (e.g., English, history, philosophy), where IDR faces fewer barriers, compared to the social and hard sciences (Lattuca 2001). Our sample of interdisciplinary scholars comprises recipients of a prestigious fellowship that is awarded to recently tenured scholars who wish to extend their research into a second disciplinary area. Sampling these successful scholars likely provides a conservative estimate of the challenges that IDR presents: if these scholars face them, certainly others do, and probably to a greater extent. Our analysis extends and complements the extant scholarship in at least two ways. First, we focus on the humanities. Most studies of IDR and its effects focus on the natural and physical sciences, where collaboration is common and IDR arises from specialists working together in teams (Fiore 2008). In the humanities, sole authorship is still the norm, so IDR is typically accomplished alone (Wuchty et al. 2007). While we do not dismiss the difficulty of working with colleagues from different epistemological backgrounds, it may still be easier than trying to learn all of that knowledge oneself. For these reasons, we study scholars in the humanities. Second, we focus on the costs of IDR to counterbalance the extant scholarship, which highlights the benefits of IDR (Frost and Jean 2003). In this way, we heed Jacobs and Frickel’s (2009) call for empirical studies of IDR’s potential negative repercussions.

What’s So Hard about IDR? Skepticism about IDR’s impact ( Jacobs and Frickel 2009) and scholarship in organization ecology (Hsu et al. 2009) suggest that domain-­spanning activities, like IDR, are penalized. We focus on possible challenges experienced during the production and evaluation of IDR, including an unsupportive environment and/or colleagues. Production Hurdles

Interdisciplinary scholars likely face hurdles during the production stage, when ideas are incubated, data collected and analyzed, and papers written. Research literature on challenges in the production phase of domain-­spanning activity focuses on coordinating and integrating contributions from team members with disparate knowledge (Uzzi et al. 2013). Coordination challenges may be less prevalent in the humanities, where sole-­authored work remains normative

New Directions, New Challenges

29

(Wuchty et al. 2007), but other production challenges remain—­like communicating across disciplinary borders and attaining mastery in two or more fields. We elaborate on each in the sections to follow. Communicating and fully engaging with members of other disciplines can be challenging—­and perhaps especially challenging—­when one works alone, and sole-­authored work is still typical in the humanities. Different intellectual domains have distinct approaches that are difficult to learn and, without sufficient immersion, can make working in another domain challenging (Strober 2011). Indeed, research that is original, significant, or sound in one discipline may appear indolent, conventional, or dispassionate in another (Guetzkow et al. 2004). Framing one’s research to align it with another discipline’s concerns, assumptions, and standards is difficult, especially without a collaborator from that discipline. Cognitive resources are finite, so mastery may also be more difficult to attain when time, energy, and effort are distributed across more than one domain (Negro and Leung 2013). Lamont and colleagues (2006) find that it is challenging for interdisciplinary scholars to master two or more bodies of literature, to accommodate the research mores and concepts of multiple fields, and to produce output that is standard in form and content. Translating unfamiliar ideas into a familiar intellectual domain takes time, but thinking as a member of a different epistemic culture requires learning tacit knowledge through experience and socialization (Strober 2011; Collins 2010). While funding for IDR has increased in recent years ( Jacobs and Frickel 2009), preparing an interdisciplinary grant proposal demands additional effort so the research can speak (and appeal) to distinct audiences (Strober 2011). At minimum, branching out to incorporate insights from other fields takes additional time: one is sacrificing the efficiency gains that come with specializing (Leahey 2006). Interdisciplinary scholars likely have to work harder than mono-­disciplinary scholars to demonstrate to two or more disciplinary audiences that their research is high quality and worthwhile. Evaluation Hurdles

Even after surmounting production challenges, interdisciplinary scholars likely face evaluation challenges. During peer review, experts from different fields often disagree on the merits of a paper and evaluate them differently (Lamont 2009; Boix Mansilla 2006). Indeed, Birnbaum (1981) finds that research that does not fit neatly within the substantive bounds of “normal science” instills irritation, confusion, and misunderstanding among reviewers and editors. Former American Sociological Review editor Rita Simon (Simon and Fyfe 1994, 34) notes that “works of imagination, innovation, and iconoclasm may fail to receive positive

30 interdisciplinary cultures and careers

appraisals from reviewers who are good at catching errors and omissions but might miss a gem, or at least the unusual, the provocative, the outside the mainstream submission.” Thus the review process may be fraught with tension and a lack of consensus and may be slower—­all of which contribute to lower rates of productivity. Research suggests that reviewers and editors tend to overlook, devalue, or outright reject domain-­spanning offerings. Such offerings are often received poorly because interdisciplinary scholars may not effectively target the relevant disciplinary audiences. Their offerings become difficult to place within existing schemas and difficult to understand, reducing their appeal (Lamont 2012). In the context of academe, this may result in lower recognition, visibility, and impact for interdisciplinary scholars unless they understand how their work fits into other disciplinary evaluation schemas. Thus, they may be caught trying to please multiple audiences and satisfying none (Lattuca 2001; Hsu et al. 2009). Scholars are evaluated not only in the review process, but also by their home institution; here again, interdisciplinary scholars might face challenges. Interdisciplinary scholars may take longer to be promoted, partly because of the lower productivity we expect to find, but also because it is hard for committee members to evaluate work outside their own discipline. Within one’s department—­and especially beyond it—­promotion committee members may fail to understand the contributions of interdisciplinary scholars. Thus, interdisciplinary scholars may experience hurdles both outside and inside their university walls. Insufficient Support

Despite the prospect of extra hurdles in both the production and evaluation of IDR, interdisciplinary scholars likely fail to receive the formal support they need to overcome them. We suspect that a lack formal support—­including financial resources as well as the backing needed to establish legitimacy—­is attributable to both disciplinary competition and organizational decoupling. Because disciplines (and their local manifestation, departments) are embedded in larger institutional fields, they struggle for resources like physical space, students, and prestige within the larger academic field (Abbott 2001). Department colleagues and administrative personnel may believe that interdisciplinary endeavors drain resources from long-­established departments, undermine their vested interests, or disrupt existing relations between disciplines (Lamont 2012; Lattuca 2001). A lack of formal support for IDR may derive, indirectly, from organizational decoupling. Policies designed to promote IDR may be implemented in a superficial or symbolic way in order to buffer existing disciplines from change (Meyer and Rowan 1991; DiMaggio and Powell 1991). Moreover, administrators may not understand how policies promote—­or fail to promote—­desired ends (Bromley

New Directions, New Challenges

31

and Powell 2012). Albert and Paradis (2014) discuss such a case. The Canadian government sought to increase the public usefulness of its academic health research by promoting interdisciplinary collaboration. It provided funds to hire humanists and social scientists into medical schools. It was assumed that they would contribute new approaches and perspectives, but this did not happen. To meet publication requirements, the humanists and social scientists ultimately conformed to the standards of the medical school by producing short articles that sidestepped social theory. Ultimately, without sufficient formal support, the hiring effort did not achieve its goals. As this example shows, interdisciplinary researchers are often caught in a lag between stated support for IDR and effectual administrative support (Lattuca 2001; Strober 2011). Interdisciplinary researchers are also likely to receive less informal support compared to their disciplinary peers. In addition to being difficult to evaluate, IDR may reflect poorly on interdisciplinary researchers, especially if they question their discipline’s epistemic foundations (Lamont and Molnar 2002). Disciplinary peers may respond to IDR they do not care for by turning their symbolic evaluations into social boundaries. Thus interdisciplinary researchers may be excluded from their discipline’s or department’s invisible colleges (Crane 1972) that serve as informal professional support networks. Unshared Cognitive-­Emotional-­Interactional Platforms

Because of production hurdles, evaluation hurdles, and a lack of support, we suspect that interdisciplinary researchers do not experience the full benefits of a shared cognitive-­emotional-­interactional (SCEI) platform (Boix Mansilla et al. 2016). That is, their relationships to scholars in their second discipline may not foster deliberation, trust, or a scholarly identity that allows for mutual learning and meaningful exchange. They may not experience solidarity with disciplinary peers that could carry them through professional frustrations. Without an SCEI platform, interdisciplinary researchers may be isolated from disciplinary peers (Lattuca 2001; Strober 2011). They may also experience role strain, an incongruence between their expectations and those of their employer, peers, or academic culture (Boardman and Bozeman 2007).

Data and Methods To investigate these ideas about the challenges of engaging in IDR, we study recipients of Andrew W. Mellon Foundation’s New Directions Fellowships. Part of the foundation’s mission involves promoting the contributions of the humanities to the well-­being of society, and the New Directions Fellowships program is one way this is accomplished. Its objective is to “assist faculty members in the

32 interdisciplinary cultures and careers

humanities and humanistic social sciences who . . . wish to acquire systematic training outside their own disciplines” (Andrew W. Mellon Foundation 2014). Each year since its inception in 2002, about twenty faculty members from the humanities or humanistic social sciences have been awarded yearlong fellowships that allowed them to work on interesting cross-­disciplinary problems. Recipients had received tenure in their home discipline and were eager to move beyond their disciplinary bounds. We sampled recipients of the New Directions Fellowships because we aimed to identify successful interdisciplinary scholars: those who engage a second discipline or specialized body of knowledge, beyond their primary discipline (National Academy of Sciences 2005). Recipients were all employed in elite research universities or liberal arts colleges, had received tenure in their home field, and were nominated by their deans as promising scholars moving in interdisciplinary directions. Of the 115 fellows to date, we selected all (thirty-­four) recipients from three disciplines (English/literature, history, and philosophy) who received fellowships before 2010. These three traditional disciplines were well represented among the fellows. We excluded fellows from less well-­ represented disciplines, like East Asian studies, to allow the possibility of disciplinary comparisons. We omitted more recent fellows (from 2011 to 2013) to ensure sufficient post-­fellowship experience with IDR. Eighteen (more than half) of these fellows agreed to be interviewed by telephone. The interviews took place between December 2012 and February 2013 and were conducted via Skype, a communication software package that offers calling capabilities, and were recorded using MP3 Skype Recorder. Of those who did not agree to be interviewed, six reported a lack of time, three expressed their disinterest in participating, two were on sabbatical, and five failed to respond to our letters and emails. Those who agreed to be interviewed do not differ systematically from those who did not in terms of their gender, discipline, institutional type, or institutional size (see Table 1.1). The interview schedule focused on faculty members’ experiences, reflections, and intellectual identities. Specifically, we asked about the interdisciplinary nature of their work; how they presented IDR to relevant to communities inside and outside their home disciplines; how IDR affected their productivity; extent of institutional support; hurdles during peer review; and possible psychological or emotional repercussions of engaging in IDR. We asked directly about frustrations, roadblocks, and other challenges that these scholars may have encountered, and probed for details. Open-­ended answers to these questions provide a rare glimpse into the world of scholarly production, risks, and reception—­one that is rarely visible in journals articles and CVs alone (Leahey and Cain 2013).

New Directions, New Challenges table 1.1

33

Comparison of Sampled and Interviewed Fellows

 

Sampled fellows, % (n)

Gender

Interviewed fellows, % (n)

 

 

Female

47 (16)

44 (8)

Male

53 (18)

56 (10)

English

21 (7)

11 (2)

History

53 (18)

61 (11)

Philosophy

26 (9)

28 (5)

Ivy League

24 (8)

33 (6)

Private (non-­Ivy)

47 (16)

33 (6)

Public

29 (10)

33 (6)

Big

41 (14)

50 (9)

Medium

41 (14)

44 (8)

Small

18 (6)

6 (1)

Discipline

Institutional type

Institutional size

We transcribed and coded the interviews between March 2013 and May 2014, and assigned each respondent a unique pseudonym. We developed a comprehensive coding scheme based on extant literature, but allowed other themes to emerge from the data during our multiple readings of the transcripts. In addition to coding their challenges, we also coded information about the scholars’ backgrounds and their positive experiences with interdisciplinarity. During the 2013–­ 2014 academic year, the first author and an undergraduate research assistant independently coded each interview and met weekly with the second author to reconcile any differences and decide on final codes. Team-­based coding is an intense and time-­consuming task that is critical for ensuring reliability and validity (Acuna et al. 2012). Due to the semistructured and reflective nature of the interview protocol, many respondents discussed their IDR efforts that occurred both before and after their New Directions Fellowship; therefore we included fellows’ reflections on their pre-­fellowship interdisciplinary work (e.g., dissertations). Because we sampled New Directions fellows only to identify successful interdisciplinary scholars within the humanities and social sciences, we do not evaluate the

34 interdisciplinary cultures and careers

impact of the fellowship program. We do note, however, that scholars’ fellowship experiences were almost universally positive. Most interviewees received formal support from their departments, disciplines, or institutions. Several talked about the encouragement, trust, and interest they received from disciplinary peers. All fellows described the collaborations, interactions, and trust they established with scholars in their secondary discipline. Recalling that this fellowship opportunity was awarded to highly successful scholars and provided resources for IDR that would otherwise be unavailable, we reiterate that the challenges we report are likely more prevalent and perhaps more severe than what we present.

Results In the following sections, we discuss the most prominent individual-­level challenges these fellows faced while engaging in IDR;1 the themes we identified and coded are in parentheses and capitalized. It would be incorrect, however, to assume that these researchers faced only challenges and difficulties. On the contrary, all respondents discussed the cognitive-emotional-interactive platforms they shared with others as a result of their IDR. Most gained additional expertise (78 percent) and borrowed new ideas from other disciplines (72 percent). Others (39  percent) explored interdisciplinary topics through their teaching. Like prior research, most (72 percent) of our respondents maintained a disciplinary identity consistent with their home discipline (Lattuca 2001; Strober 2011). Although the challenges we describe make IDR difficult, they appear surmountable. Quite Simply, It’s Hard!

Disciplines put boundaries on what is “real,” accessible, and relevant (Abbott 2001), and this makes crossing those boundaries a real challenge. Shira described traversing the distinct approaches to knowledge acquisition that one finds in psychology and philosophy: “So empirical psychology is discourse of enterprise. So philosophical investigation is sometimes a meta-­descriptive enterprise and sometimes a normative enterprise . . . the authors [in psychology] tend, in certain traditions [to] distract away from particulars . . . [and] to try to figure out what parts are, and what parts ­aren’t relevant is sometimes a challenge.” Indeed, 89 percent of New Directions fellows we interviewed noted the extra cognitive effort (hard) required to integrate knowledge across disciplinary boundaries (see Table 1.2). For example, when we asked David, a philosopher, about the challenges he faced integrating law into philosophy, he mentioned the vast amount of additional reading that was required to get up to speed: “The amount of material on any given topic is just bewildering and there isn’t enough

New Directions, New Challenges table 1.2

35

Challenges of Engaging in Interdisciplinary Research

Code

Description

% 1+ mentions

Total # mentions

frame

Extra burden to frame work for audience(s)

89

57

hard

Increased cognitive work

89

54

resp

IDR requires extra commitment and responsibility

78

47

time

IDR takes longer; need more time

78

28

no support

Home discipline not receptive to integrative effort

61

36

prod

Fewer academic publications

44

15

no fund

Difficulty securing funding

17

4

no recog

Fewer awards/recognition/visibility

17

4

Slower advancement

11

6

slow

time in the day to read it all, that’s the main difficulty. It is not otherwise a challenge to integrate the subjects—­they’re close enough.” Asked if she could claim expertise in her second discipline, Mary said, “Well, I wouldn’t go that far. I think I am a much more able ‘channeler’ of what’s going on in that area, but I wouldn’t publish in a theology journal. I don’t feel I’ve become a complete native in that environment.” Niels had similar experience; though others may perceive him as an expert in his new field, he still claims greater mastery in his home field. “I don’t feel expert enough. I’ve become enough of an expert to other people that some other people treat me as an expert. I get asked to give talks now in related areas that I wouldn’t have been before and I even have students or at least one student who is working with me. . . . Yes, I am getting closer but I still don’t feel as much a master of this material as I did the material I was working on as a PhD student.” Sometimes, even knowing how to begin learning from another discipline was difficult. John, a historian, described the difficulties he had just “learning how to learn” in a new field: “[The new area was] something I didn’t know anything about, something that I didn’t know how to study even if I had the technical ability to study.” Cognitive challenges may be heightened for scholars in the humanities, where collaboration is uncommon and individual expertise, rather than team-­based knowledge integration, is required (Fiore 2008). For them, IDR means not only working in an unfamiliar domain, but doing so in isolation, as Niels describes. Having been turned away by a potential mentor in his second discipline, he said, “I just sort of found myself a bit in a gap because the material was hard enough that I just really wasn’t able to teach myself quickly enough.”

36 interdisciplinary cultures and careers

Later in the interview, he described how these difficulties continued, due to a lack of interested peers in his home department. “There isn’t much mathematics in the air around me and so it’s—­let me put it this way—­it’s much harder to learn a foreign language when you’re in the United States than when you just go and immerse yourself in the other culture . . . other people in my department don’t talk about [mathematics], so I feel like it’s just hard to become fluent in the language. That’s a real challenge.” Integrating knowledge across disciplinary lines may not be fully realized without an insider’s perspective in the other discipline, which may require extensive training, immersion, or guidance from peers (Guetzkow et al. 2004; Lamont et al. 2006). In contrast to David’s experience, Shira had difficulty making full use of legal material as a philosopher. She said it requires extensive background knowledge: “You need to learn how to read a case or to read a well-­reviewed article with the kind of eyes that a lawyer has. You need to learn ordinary legal doctrine in order to make sense of the framework in which these guys are writing and the bar to entry is pretty high.” Course work, by itself, is not enough; Renaud, a historian, told us about how his research took him outside of the classroom. “I also met with faculty and scholars working in the [new] field, put together reading lists, and got myself working on that. And [I] ended up working closely with a graduate student I met through the process who is a music theorist and composer and interested in the . . . topic that I was working on. So there was a lot of also informal learning that went on as a result too.” While some scholars used their fellowship year to engage in formal course work to learn the foundations of a new discipline, almost all noted the importance of individual study and the tacit knowledge gained from communication with members of that discipline. Roadblocks continue even after scholars gain a foothold in another discipline. Even after acquiring (what they hoped amounted to) an insider’s knowledge of their second discipline, some fellows found the habits of thought (Strober 2011) established in their home discipline interfered with their interdisciplinary efforts. An English professor, Jessica, described how she came to know the different epistemological roots of her home discipline and neuroscience. “As an English professor what you learn is that interpretation is everything. Scientists know that interpretation is everything but that you have to be much more constrained in the extent to which your interpretations can shape the claims that you made. An interpretation isn’t a claim for a scientist. An interpretation is a claim for a literary scholar. And restraining that is kind of hard.”

New Directions, New Challenges

37

IDR Requires Extra Commitment

Though unprompted during interviews, numerous fellows expressed their dissatisfaction with IDR that is poorly conducted. They recognized that ideas from other intellectual domains can be misrepresented as they are passed on to unsuspecting disciplinary audiences (Boix Mansilla 2006). As Daniel, a historian noted, “All of us have read things where scholars of one field try to incorporate another discipline into their own and failed miserably because they make fundamental errors about how another field works. I think the danger in dabbling in anything interdisciplinary is that if you skim the surface . . . [you could say] something really obvious or banal or insulting about another field before really attempting to engage it seriously.” Renaud also worried about this possibility: “How well am I going to be able to do this kind of analysis where I’m trying to pull a lot of types of approaches that . . . many people have spent lifetimes working in?” Most New Directions fellows we interviewed said that they worked hard to move beyond mere surface-­level engagement with other fields. Specifically, three-­quarters (78  percent) of respondents noted the extra commitment and responsibility that thorough IDR requires (resp). Daniel described his efforts to stay current with rapidly-­changing trends in a medical field: I subscribe to several different blogs, and I’m heavily involved in writing right now so I’m actively keeping abreast to what’s going on. And like all scholars do, I subscribe to tables of contents, various journals, and I’ve also made contacts with people I keep in touch with. So through that network and just keeping up with publications in the area in which I’m working is the only way to do it. Once I’m done with this [interdisciplinary] book, I won’t [work] as fervently as I am now to keep current. But right now I’m doing everything I can.

Renaud said that he was concerned to make sure “that I do justice to what I am trying to do.” For Jordan, a philosopher, the standard for thorough interdisciplinary work was publication in the journals of one’s secondary discipline: In my mind, if you are a philosopher and you are interdisciplinary, then you should be able to publish in journals of the discipline that you’re engaged with. So if you’re doing philosophy of science with respect to genetics, is it possible for you to publish your papers in Nature Genetics? Or is it you only publish in philosophy journals but you talk about genetics. To me, that’s not necessarily interdisciplinary. . . . I have several papers in science and medical journals, in journals that are not philosophy journals.

38 interdisciplinary cultures and careers

Indeed, this extra commitment can help maintain interdisciplinary engagement. When asked about what it was like to take classes as a student again, Elizabeth mentioned that she thought that many professors refrained from interdisciplinary work because it threatened their professional identity as an expert. To really delve into interdisciplinary training, she said, she “needed to be willing to become a learner again, to not be good at things.” She is not alone; 26 percent of all respondents shared similar sentiments. When asked how he used the fellowship to further his research agenda, Renaud said, “There is something terrifying and useful about being a student again, you know grappling with things that are way too hard for you.” When we asked Jessica, an English professor, what advice she would give to others seeking to do similar IDR, she said “you have to have . . . a willingness to look stupid and then just test it.” From these excerpts, the added responsibility and commitment of good IDR is palpable. IDR Is Time-­Consuming

Susan, when asked about the challenges she faced integrating her second discipline into her home discipline, said, “It’s time. It’s just time.” Most (78 percent) of our scholars had similar experiences and indicated that their interdisciplinary work required more time to conduct (time). Reading and learning new material from another discipline is more difficult, as indicated earlier, but these challenges are amplified when they compete with existing professional obligations. Susan, a historian of science, described this: I think my difficulties are largely the difficulties shared by many. It’s just as academic life becomes increasingly busy. . . . I do think there is a distinct challenge in being an interdisciplinary-­minded person in a department that’s not overall oriented that way. Just to give another example of a big challenge, we’re going through graduate applications right now. Now, an extremely small fraction of those applications will be in my field or a closely related [field] to mine . . . so it’s just sort of a mental challenge to think hard about those issues, one hour, reviewing applications, and the next hour do research and mathematical things.  .  .  . I really love being able to think about lots of different topics, but . . . I feel like sometimes there’s just not enough time.

Learning a new field takes time, especially when scholars are committed to doing it well, and thoroughly. In this way, we see that time and commitment are somewhat contingent on each other. Mary, a historian, described becoming a theologian as “a very long formative process.” When asked what advice he would give to someone following in his shoes, Paul, a historian, said, “Don’t get frustrated. Languages like [the one I learned] don’t come as fast as they should. Something

New Directions, New Challenges

39

that takes you twenty-­five years just to be at the beginning.” Meeting the added responsibility of IDR and maintaining its integrity, in the words of John, “required a deeper investment of time to go beyond the surface, to get beyond what other interdisciplinary researchers were willing to do.” Earlier in his interview, John distinguished his efforts from previous attempts to span European history and Eastern studies. He said, “A lot of people were interested in bringing the two worlds together . . . but most people doing that were not necessarily willing to invest the time and effort into actually learning the languages of methods required to do that in an intellectually serious way.” Some New Directions fellows claimed their IDR took longer because they spent more time checking claims from the secondary discipline. Although only 17  percent of respondents explicitly mentioned fact-­checking, it is an intense challenge. Jessica describes the extra time she took checking scientific claims before presenting her work to audiences in the humanities: “I try to be very clear in the claims that I am making and the claims that I am not. I try to read a lot of the critical counter-­discourse and address the kinds of concerns that they have had. As I said I want to make sure that I dot every ‘i’ and ‘j,’ and cross every single ‘t’ that needs to be crossed so that people can’t make any objections to the substance of my work, just the interpretation of it.” IDR Takes a Toll on Productivity

Because learning and incorporating ideas and methods from other disciplines take time, a substantial number of fellows (44 percent) experienced slower productivity (prod) that resulted in fewer publications. John said, “If I think about the number of articles or book chapters or whatever that I could have written, instead of studying [my secondary discipline], it’s immense, right? I’ve cost myself, in terms of pure scholarly productivity, a great amount of research.” Niels commented on the cost of teaching himself a branch of mathematics: “Actually, if one looks at my research output—­for the sake of my career promotion and advancement I don’t try to say this too loudly—­but there’s actually a big gap. It looks like I went into a coma or something.” In particular, writing becomes inefficient. Renaud, commenting about the benefits and drawback of adding new methodological approaches from his second discipline to his research repertoire, said, “Interestingly, it has probably slowed down some of my writing. Because the book I’m writing now is probably more ambitious some ways in scope.” Mary had a similar experience and discussed how much longer it takes her to generate an article in her interdisciplinary area. When asked if engaging another discipline hindered her ability to produce high-­impact scholarship, she said, “I wouldn’t say difficulty, but I would think about the time frame. [Part of why it takes] longer to go from conception to a finished product has to do with trying to think through two separate disciplinary concerns.”

40 interdisciplinary cultures and careers

Lack of Support

Most of the New Directions fellows we interviewed (61  percent) were discouraged by a lack of support for their interdisciplinary work (no support). Funding was not a major concern; only 17 percent of respondents mentioned a dearth of research funds (no fund). Rather, the fellows were concerned with interpersonal and administrative support. They were discouraged by disciplinary peers who pushed back against their interdisciplinary efforts. Sometimes fellows were compelled to justify or explain their IDR in the face of criticism. Cynthia, a historian, describes how members of her field viewed her interdisciplinary direction. She said, “People understood—­they didn’t understand—­ they understood what I was writing about, they didn’t quite understand why I wanted to take the tack that I did to it. Why I was using philosophy, and why would anyone be interested in philosophy in relation to physics?” Niels, a philosopher eager to integrate mathematics into his research, describes being teased by a department colleague after learning of his fellowship award: “He jokingly said to me, ‘What new thing are you going to learn for the Mellon scholarship? Are you going to learn philosophy?’” The premise here is that a philosopher so focused on mathematics no longer “knows” philosophy and has to relearn it. Later when we asked Niels whether he felt like he had a home discipline, he described the lack of informal social support he received from his home discipline: “I definitely feel a bit alienated in mainstream philosophy. And so I don’t think that will change in the near future. Philosophy is a very diverse discipline and yet, most of it finds itself in [the] humanities, where I feel much more on the scientific side of things.” At times, scholars’ lack of support stemmed from organizational decoupling (Bromley and Powell 2012; Meyer and Rowan 1991). Paul, when asked about whether his institution was supportive of interdisciplinary work, said, “I don’t think so. I have to be blunt; I don’t think it is. The previous dean used that language, but that was really a way of cost-­cutting and controlling faculty and controlling the chairs. It was more used to manipulate programs.” In his view, interdisciplinary support was utilized to maintain administrative power. When probed for more detail, he mentioned that his dean’s conception of “interdisciplinarity” stemmed from the hard sciences. The dean combined the humanities into odd configurations, and encouraged such scholars to pursue team-­based projects. He continued, “As a historian I would actually argue we don’t necessarily work in groups. So it [the reorganizing of departments] certainly has been used at my institution [as] an old science model—­which is, you have to work with other people in a team or in a group. . . . For me, interdisciplinary is always an argument for an individual mind reading widely . . . as opposed to a structural notion.”

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Although their interdisciplinary work was a struggle, these scholars often found some measure of support. Of those who voiced concerns, 45 percent also mentioned encouraging colleagues, supportive contacts from their second discipline, or helpful administrators who pushed their research along. To be productive, our scholars took what support they could find, even if it was not always enough. Finding the right audience for their work and winning them over was another challenge altogether. Framing

Almost all (89 percent) fellows are aware of epistemological differences between disciplines, and are continually challenged to frame their interdisciplinary scholarship as legitimate and interesting to others (frame). Scholars embedded in disciplinary cultures must be convinced that unfamiliar ideas, subjects, and methodologies are interesting, valid, and appropriate. As Justin put it, the question is “are you more interesting than different, or different than interesting? . . . You’re doing something that, in someone’s lights, is seen as naïve. You feel like an idiot when you expose yourself to being judged by other standards.” Making valued contributions to multiple disciplinary audiences is not a smooth process; interdisciplinary researchers run the risk of criticism from both sides. When we asked Samuel, a historian, how his interdisciplinary dissertation was received by historians and academics in other fields, he summarized his concerns succinctly: “There were quite a few years that my nightmare was that the Jewish historians would dismiss it as the work of a Russianist, and the Russianists would dismiss it as the work of a Jewish historian.” New Directions fellows took pains to circumvent perceptions of diluted quality and the resulting devaluation that besets domain-­spanning efforts like IDR (Hsu et al. 2009). For example, Sarah, a historian, requested friendly reviews from colleagues before sending her papers to a journal. She described her colleagues as being “quite nervous dealing with stuff that doesn’t fit . . . and the more you try to sort of incorporate different ways of approaching questions the more uneasy people tend to become.” Later in the interview, she described her journal submission experience. She said, “That got sent out and predictably the people who read it said ‘Who is this?—­It’s too hard. I don’t know what’s going on here. This is too complicated . . .’ and I ended up having to write a kind of introduction to that so that . . . the editor of the journal who actually wanted it would be satisfied that it wasn’t too daring.” Many New Directions scholars successfully spanned disciplines. When asked how he learned to bridge two distinct audiences, Samuel, a historian, said, “One key factor that I sensed early on that if you could demonstrate linguistic competence for both groups—­that was a very big step to gaining

42 interdisciplinary cultures and careers

credibility. Because the first sign of someone who is an interloper in your area, they don’t really have the lands down. So I worked very hard to make that happen and I tried to make sure that I attended conferences and presented my work to both kinds of audience consistently.” These audiences also provided him with constructive feedback. He said, “I got very helpful feedback both from audiences and individuals who read drafts of my work. I tried to get my stuff read as widely as possible before it was published. And to make sure that I was familiar with the animating divots of these various fields, that I could speak their dialects, so to speak.” Samuel was not alone. Jordan, a philosopher, strategized when trying to publish in philosophy, statistical, and medical journals. For him, connecting to these disconnected audiences involved explaining the value of his research to each. In his words, You want the audience in those disciplines to be able to consume the work that you’re doing and to benefit from it and it’s hard because you’re now coming at them with concepts/ideas/arguments/materials that a­ ren’t familiar to them. So too, it’s for people in your own discipline to get their mind around what you’re doing, probably because you’re not talking to them. You’re talking to other people or scientists and you may be using tools or a framework that they’re not familiar with. So I think that’s a challenge for all the parties there, who often don’t speak to each other, to recognize that there’s value in what you’re doing.

Fellows like Jordan, who desired to speak to new audiences, were able to do so, provided they incorporated feedback, cultivated their awareness of disciplinary differences, and took care to translate their ideas into the local dialect.

Conclusion In this study, we critically assessed the professional challenges of engaging in IDR by interviewing eighteen recipients of the Andrew W. Mellon Foundation’s New Directions Fellowship, which supports faculty in the social sciences and humanities to pursue training in another discipline for one academic year. All fellows were successful in their home discipline while pushing their research forward in an interdisciplinary direction. Perhaps this is because the social sciences and humanities have fewer formal barriers to conducting interdisciplinary work than the natural sciences where teams are necessary for large, costly, and complex projects (Lattuca 2001; Wuchty et al. 2007). The scholars we interviewed may have more positive experiences with IDR than a broader set of faculty would. Thus, the challenges they report provide a conservative glimpse of the difficulties and frustrations other interdisciplinary scholars face.

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Our conversations reveal that IDR requires extra cognitive effort and additional time to produce. The uniqueness of such integrative efforts makes the resulting work difficult to evaluate and, often, to accept. Even these successful interdisciplinary scholars faced discouragement from their institutions and disciplinary peers. However, their sense of commitment and responsibility to high-­quality IDR carried them through these social, emotional, cognitive, and professional challenges. To a large extent, the scholars we talked to were able to overcome many of these challenges, moving their research forward despite increasing professional obligations and insufficient support. Perhaps because these tenured fellows were established in their home fields, we got little sense that the challenges they encountered translated into professional risks. The challenges we identify are real and often disconcerting, but this elite group of fellowship recipients was well positioned to, with some effort, overcome many of them. Fewer than half of them voiced concerns about reduced productivity, and few (17 percent) were challenged by a lack of funding (no fund) or were concerned about scholarly recognition (no recog). Only two scholars (11 percent) mentioned slower career advancement (slow). Our analysis shows that IDR is as challenging as, but perhaps less risky than, we anticipated—­at least for these established scholars. For young PhDs aiming to establish their professional identities and secure tenure-­track positions, the professional risks may outweigh the rewards (Rhoten and Parker 2004). We encourage future research to focus on challenges faced by junior faculty, less-­visible scholars, and graduate students. Like Lattuca (2001) and Strober (2011), we found that interdisciplinary researchers desire more support for their research. Our interviews also make clear that increased funding for IDR, while welcomed, is not a panacea. Interdisciplinary scholars need additional time to conduct research (e.g., release from teaching or reduction in service) and more opportunities to foster supportive networks. Yet in many academic institutions, statements of support from the administration are still decoupled from actual practice. This is likely to continue because there are many “trading zones” that require support and coordination (Galison 1999). For example, the fellows we spoke with discussed not only their research collaborations, but also teaching efforts and personal explorations into a second discipline. In developing their policies, administrators should attend to each of these arenas, and the complex network of relations among them. Finding more ways, especially immaterial ways, to support interdisciplinary researchers should mitigate some of their challenges. Some institutions have done this via cluster hiring, which mitigates the real possibility that interdisciplinary scholars feel isolated. Institutions might also be encouraged to revise their promotion and tenure guidelines and help educate promotion and tenure committee members about the additional challenges

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associated with IDR so that they can better evaluate interdisciplinary candidates. Professional association, colleges, and departments can offer workshops and mentoring programs to provide a supportive environment, mutual understanding, and knowledge sharing (Collins 2010; Uzzi and Lancaster 2003). Scholarly peers may encourage interdisciplinary researchers’ informal explorations into other disciplines, even if these explorations appear uncertain, intractable, or threatening at times. In addition to insufficient support from the powers that be, interdisciplinary researchers experience “bottom-­up” discouragement from their disciplinary peers who may not understand, appreciate, or support interdisciplinary work. While an SCEI platform is ideal (Boix Mansilla et al. 2016), our results suggest that it is difficult, and perhaps rare, to attain. Interdisciplinary researchers operate between, and indeed straddle, often-­incompatible SCEIs. Future research might explore the strategies interdisciplinary scholar use to attain legitimacy in different disciplines. Our results beg the question, given the difficulty, why do scholars pursue IDR? It may be that interdisciplinarity resonates with scholars’ identity, or “intellectual self-­concept” (Gross 2002). Or perhaps those who occupy high-­status positions in one field may—­because they have cultivated a habitus that is durable and transportable—­find it “natural” to seek success in their secondary discipline (Bourdieu 1984) and have developed the skill to do so. Scholars may also realize the limits of their primary field and turn to another discipline to better address their research problem (Lattuca 2001). Most (89 percent) of the fellows we talked with expressed this concern. One historian, for example, was determined to learn more about science to provide a deeper history of science: “I have also sort of been weirdly fascinated with nuclear things. Why did people make decisions back in the fifties and sixties and seventies that now seem so incredibly stupid? I mean, truly, there was no answer to that. I thought, ‘If I’m interested in nuclear stuff, and I have enough science—­if I can learn nuclear engineering—­ then I might actually be able to give an account of this.’ ” And last, though not a focus of our chapter, engaging in IDR can also be beneficial (Lattuca 2001; Strober 2011). Without much prompting, the fellows we interviewed discussed the positive emotions, social interactions, and recognition they received from their IDR engagement. Most scholars became more open-­minded (66 percent), gained expertise (78 percent), and contributed ideas to their second discipline (56 percent). For these, and probably other reasons, scholars continue to pursue IDR even without support. Soon, science policy and especially practice might catch up with their efforts and ameliorate the various challenges we identified here.

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note

We would like to thank the University of Arizona’s Social and Behavior Sciences Research Institute for a Faculty Research Grant to support this research, Edward Lee for research assistance, and Mathieu Albert, Barbara Prainsack, Scott Frickel, Corey Abramson, and Megan Henley for constructive comments on earlier drafts of the chapter. 1   Initial analyses of the data using correspondence analysis found few if any disciplinary dif-

ferences, so we speak about fellows’ experiences holistically.

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46 interdisciplinary cultures and careers Gross, Neil. 2002. “Becoming a Pragmatist Philosopher: Status, Self-­Concept, and Intellectual Choice.” American Sociological Review 67 (1): 52–­76. Guetzkow, Joshua, Michele Lamont, and Gregoire Mallard. 2004. “What Is Originality in the Humanities and Social Sciences?” American Sociological Review 69 (2): 190–­212. Hsu, Greta, Michael T. Hannan, and Özgecan Koçak. 2009. “Multiple Category Memberships in Markets: An Integrative Theory and Two Empirical Tests.” American Sociological Review 74 (1): 150–­169. Jacobs, Jerry A., and Scott Frickel. 2009. “Interdisciplinarity: A Critical Assessment.” Annual Review of Sociology 30: 43–­65. Lamont, Michele. 2009. How Professors Think: Inside the Curious World of Academic Judgment. Cambridge, MA: Harvard University Press. ———. 2012. “Toward a Comparative Sociology of Valuation and Evaluation.” Annual Review of Sociology 38: 201–­221. Lamont, Michele, Gregoire Mallard, and Joshua Guetzkow. 2006. “Beyond Blind Faith: Overcoming Obstacles to Interdisciplinary Evaluation.” Research Evaluation 15 (1): 43–­55. Lamont, Michele, and Virag Molnar. 2002. “The Study of Boundaries in the Social Sciences.” Annual Review of Sociology 28: 167–­195. Lattuca, Lisa R. 2001. Creating Interdisciplinarity: Interdisciplinary Research and Teaching among College and University Faculty. Nashville: Vanderbilt University Press. Leahey, Erin. 2006. “Gender Differences in Productivity: Research Specialization as a Missing Link.” Gender & Society 20 (6): 754–­780. Leahey, Erin, Christine Beckman, and Taryn Stanko. 2015. “Prominent but Less Productive: The Impact of Interdisciplinarity on Scientists’ Research.” Working paper. http://arxiv. org/abs/1510.06802. Leahey, Erin, and Cindy L Cain. 2013. “Straight from the Source: Accounting for Scientific Success.” Social Studies of Science 43 (6): 927–­951. Meyer, John W., and Brian Rowan. 1991. “Institutionalized Organizations: Formal Structure as Myth and Ceremony.” In The New Institutionalism in Organizational Studies, edited by W. Walter Powell and Paul J. DiMaggio, 41–­62. Chicago: University of Chicago Press. National Academy of Sciences. 2005. Facilitating Interdisciplinary Research. Washington, DC: National Academies Press. Negro, Giacomo, and Ming D. Leung. 2013. “‘Actual’ and Perceptual Effects of Category Spanning.” Organization Science 24 (3): 684–­696. Porter, Allan L., and Ismael Rafols. 2009. “Is Science Becoming More Interdisciplinary? Measuring and Mapping Six Research Fields over Time.” Scientometrics 81 (3): 719–­745. Rhoten, Diana, and Andrew Parker. 2004. “Risks and Rewards of an Interdisciplinary Path.” Science 306 (5704): 2046. Simon, Rita J., and James J. Fyfe. 1994. Editors as Gatekeepers: Getting Published in the Social Sciences. Lanham, MD: Rowman & Littlefield. Strober, Myra H. 2011. Interdisciplinary Conversations: Challenging Habits of Thought. Stanford, CA: Stanford University Press. Uzzi, Brian, and Ryon Lancaster. 2003. “Relational Embeddedness and Learning: The Case of Bank Loan Managers and Their Clients.” Management Science 49 (4): 383–­399. Uzzi, Brian, Satyam Muhkerjee, Michael Stringer, and Ben Jones. 2013. “Atypical Combinations and Scientific Impact.” Science 342: 468–­472. Wuchty, Stefan, Benjamin F. Jones, and Brian Uzzi. 2007. “The Increasing Dominance of Teams in Production of Knowledge.” Science 316 (5827): 1036–­1039.

2 ◆ The Fric tions of Interdisciplinarit y The Case of the Wisconsin Institutes for Discovery G r e g o r y J. D o w n e y, N o a h W e e t h F e i n s t e i n , D a n i e l L e e K l e i n m a n , Si g r i d P e t e r s o n , a n d C h i s ato F u k u d a

In November 2004, the governor of Wisconsin, together with the chancellor of the University of Wisconsin–­Madison, held a high-­profile press conference to announce a major new scientific research and economic development initiative in “the superheated world of biomedical research,” dubbed “The Wisconsin Institute for Discovery” (Devitt 2004). Though the details were vague at the time, the venture was meant to draw on both public and private funds, to engage in both basic and applied research, and to sit at the interdisciplinary intersection of science, medicine, and technology. Six years later, in December 2010, the dream became reality when a new state-­of-­the-­art research building opened its doors for the first time. More than simply a new set of classrooms, offices, laboratories, and meeting rooms, the Discovery Building, as it came to be called, represented an intertwined set of claims about the future of the research university in an environment characterized by an increased public need for scientific knowledge, decreased public funding for knowledge production, and contested public support for scientific findings. These claims—­ promoted as “best practices” for designing the organizational, financial, material, and intellectual infrastructure of scientific innovation—­relied upon a positive vision of interdisciplinary research as the inevitable and transformative frontier 47

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of academic knowledge production (e.g., National Science Foundation 2011; Klein 2010; Andreasen and Brown 2005). But “interdisciplinarity” is a slippery term—­it is both a spatial and a social metaphor, to which different actors constantly apply and reapply particular understandings. This chapter builds on the historical and critical literature that explores how interdisciplinarity is actually understood, instantiated, mobilized, and measured in scientific research (e.g., Jacobs 2013; Jacobs and Frickel 2009; Sá 2008; Frodeman and Mitcham 2007; Weingart and Stehr 2000). Like much of the existing literature, we focus on a moment of imagining interdisciplinarity anew (e.g., Jeffrey 2003; Rhoten 2004), not a case of integrating interdisciplinarity into long-­standing, powerful organizational structures (e.g., Albert et al. 2015). But rather than offering a single definition of interdisciplinarity ourselves—­or attempting to evaluate the outcomes of interdisciplinary scholarship—­we provide a detailed portrayal of how researchers and administrators define and redefine interdisciplinarity in service to their own evolving goals and needs, by instantiating those spatial and social metaphors into durable material and organizational structures. Through a close investigation of how the Discovery Building was planned by its designers, promoted by its advocates, and inhabited by its initial group of scientists—­based on oral histories, documentary records, and participant observation—­we argue that the inevitable “frictions” surrounding scholarly training, affiliation, and practice continually challenge and complicate the overall idea of interdisciplinary progress. Interdisciplinary dreams may be made durable, but they are rarely entirely fulfilled. The new Discovery Building was initially promoted as an interdisciplinary intellectual project in two senses. On one hand, it would host research on a “fixed” problem domain centered around one specific (and controversial) target area of research—­stem cell science—­which, it was argued, demands an interdisciplinary approach combining expertise in materials, medicine, and mathematics. On the other hand, it would provide a home for unforeseen, “unfixed” interdisciplinary science, through the interaction between three cohabiting more or less disciplinary research areas: nanotechnology, biotechnology, and information science. The challenge, then, was to create a place that would not only serve as a container for this hybrid intellectual project, but also attract and retain the best scholars available to pursue that intellectual project, resulting in a “transdisciplinary crossroads” for the whole campus (Wisconsin Institute for Discovery 2014). Yet the resulting design of the Discovery Building reflected more than just this hybrid intellectual innovation in its dream of interdisciplinarity. Three further innovations were advanced. The first was a hybrid organizational structure. The building itself housed three related but distinct entities, which we call the “Discovery Institutes”: (1) a public research center of the UW–­Madison, called

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the Wisconsin Institute for Discovery (WID), occupying one half of the research space; (2) a private nonprofit medical research organization, the Morgridge Institute for Research (MIR), occupying the other half of the research space; and (3) a quasi-­public outreach and conference facility called the Town Center, filling the entire first floor of the building. Although many different rationales were latter attributed to this hybrid structure, the pairing of public and private institutes was initially driven by the desire for a legal haven for stem cell research, free from legislative restrictions on the use of public funds. The second innovation was a hybrid financial structure. Funding for the three organizations making up the Discovery Institutes came from both public and private sources. Public funding included local city and county tax breaks, state bond approval, university core budget revenue from the state, and federal research grants. Private funding included gifts from wealthy individual donors, contracts with for-­profit corporations, and intellectual property investment revenue from the Wisconsin Alumni Research Foundation (WARF), the UW–­ Madison’s private, nonprofit outside patent and licensing agent (see Apple 1989). This financial structure was intended to allow knowledge production to proceed even in an environment of economic uncertainty and political controversy. The third innovation was a hybrid material structure to house the three Discovery Institutes: a new shared building divided among the various organizations in a legal condominium arrangement. The design of this building was meant to support a new kind of interdisciplinary, public-­facing, and results-­oriented science by (1) housing a multitude of different researchers, labs, and graduate students each affiliated with different UW–­Madison home departments and disciplines; (2) purposefully bringing those researchers into daily contact through architectural design encouraging informal “collisions,” creative serendipity, and sustained scientific collaboration; (3) inviting different publics to interact with these researchers through a myriad of Town Center events; and (4) providing space for entrepreneurial and translational training and administration for marketable discoveries on-­site. In its intellectual design, the Discovery Institutes were intended to be a compelling example of interdisciplinarity and the new public-­facing, market-­aware, and interventionist orientation to science (see Gibbons et al. 1994). But our findings suggest that it is a mistake to think about interdisciplinarity as only an intellectual project; instead, the four hybrid structures we have identified—­ intellectual, organizational, financial, and material—­work together in complex and sometimes contradictory ways. Our informants think about interdisciplinarity at many scales and across many dimensions—­interpersonal, geographical, institutional, and professional—­and our data suggest many ways in which interdisciplinarity is entangled with other social projects that shape the contemporary practice of science—­such as the state emphasis on regional economic

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development, academic debates over the allocation of scarce public resources to higher education, corporate imperatives to bring profitable medical interventions to market, and public concerns about scientific controversy. While much research has pointed to the challenges of realizing interdisciplinarity in practice, thinking about interdisciplinarity in the context of these dimensions helps reveal why the idea of interdisciplinarity remains fluid—­an ongoing source of tension and contradiction. Our primary analytic theme, then, is that this particular case of interdisciplinarity cannot be understood as an intellectual project or knowledge-­making initiative in isolation from other important social projects. From the very start, the idea of interdisciplinarity was deployed by those involved with the Discovery Institutes in multiple competing and sometimes contradictory ways during the building and early development of the Discovery Institutes. One might expect a large-­scale interdisciplinary effort to require a coherent operationalization, or at least a shared vision, of interdisciplinarity. Instead, many such visions percolated through the design and development of the Institutes, licensing and justifying different sorts of activities without ever (in the scope of our research) prompting any sort of top-­down efforts to define interdisciplinarity. In addition, although we describe the Discovery Institutes in terms of innovations, their design and development were characterized by borrowing and repurposing of existing ideas and mechanisms from the UW and across the globe. After showing that those involved with WID did not operate with a single, coherent, a priori definition of interdisciplinarity, we devote the second part of our chapter to considering how the move to a distinct building physically embodying diverse visions of interdisciplinarity resulted in a host of strained interactions—­or “frictions”—­as the new and old became accustomed to each other. In the following sections, we draw on dozens of structured interviews with Discovery Institutes designers, occupants, and stakeholders, as well as press accounts, internal documents, and ethnographic data, gathered over multiple years.1 Importantly, our chapter is about how people think and talk about interdisciplinarity. We advance no claim of our own about what interdisciplinarity is, whether it is actually being enacted in the Discovery Building, or whether it is a superior form of epistemic practice. Instead, our position is that interdisciplinarity should be understood as a particular way people think and talk about scientific research. Such talk is often both metaphorical and strategic: developed in different ways and deployed for different purposes by different actors within the academic division of labor. We first examine the design, construction, and population of the Institutes—­ their intellectual, material, and organizational origins—­showing how interdisciplinarity is discussed and performed in different ways by different actors in shared circumstances. (The intriguing complexity of the financial origins, and

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their implications for higher education more generally, are the subject of a separate analysis currently underway; see Kleinman et al. 2013 for some initial work.) Then, we turn to the Institutes’ operations, focusing, in particular, on the “frictions” that arose across the space and time of research teams, departmental affiliations, and individual careers. In both sections, each innovation emerges as entangled with the others and dependent on earlier innovations and social arrangements. In both the design phase and the operation phase, “interdisciplinarity” was repeatedly reconceived and redeployed, as both a strategic goal and an evocative metaphor. Thus, although “interdisciplinarity” may have served as a powerful catalyst for scientific knowledge production—­at least in this story—­it is ultimately a poor operational yardstick for assessing scientific value.

Space for Sparks and Collisions, and People to Provide Them Very little was established about the nature of the Discovery Institutes when the architectural process began. The physical home and the programmatic nature of the Institutes evolved in parallel. Since neither the precise nature of the research nor the identity of the researchers was known, the building committee’s guiding principle was flexibility. According to one of the architects, they were forced to enlist “a series of surrogate user groups on campus and around the country” to stand in for the eventual researchers in terms of design guidance (interview 2010-­08-­27). It was a novel problem: “How do you program for a building with just a vision and no real occupants?” (interview 2010-­06-­14). The high-­minded solution to this problem was to design the building around a generic vision of interdisciplinary science, rather than building it for particular disciplinary tenants. The design/build team visited a series of prestigious, interdisciplinary university research institutes with the ambitious goal of ensuring that the UW–­Madison institute would be seen by the research community as the best of them. Intriguingly, this external benchmarking effort was accompanied by a local benchmarking effort, in which both the building and program committees were asked to consider other buildings and interdisciplinary programs at the UW–­Madison. Large-­scale features of the eventual Discovery Building, such as the prominent atrium and the division of lab space into thematic research areas, had precedents both on campus and off. Looming large in the design process was the long-­standing architectural dream that the physical arrangement of space could encourage interdisciplinary behavior (e.g., Yaneva 2010). In particular, the Institutes were designed according to a specific vision of interdisciplinary science: “instead of isolation of scientists to do narrow, deep, creative work, there was a need to foster interaction, socialization, exchange of knowledge in an informal way” (interview 2010-­08-­31).

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The architects thought about the building as a place where breakthroughs could happen “over a cup of coffee, by a chance coincidence” (interviews 2010-­08-­18, 2010-­10-­07, 2010-­06-­14). These chance meetings were referred to in an entirely positive way as “collisions.” To foster such collisions, the building committee decided that the edifice should encourage (if not enforce) what one of the architects called “casual places for interaction” between the diverse resident scientists (and to a lesser degree, between the resident scientists and various building visitors) (interview 2010-­08-­27). Not entirely content to leave the matter to chance, the architects included a couple of forcing devices, the most notable of which involved locating all of the bathrooms on one side of the building, with the idea that scientists from one end would be obliged to track past other labs on their way to meet the call of nature. In designing a building for interdisciplinarity through chance encounters, the building committee realized that there could be tension between the social space designed to encourage interaction and engagement and the scientists’ need for private space. As one of the lead architects put it, the Discovery Building had to also support “privacy, the ability to . . . step away from the big environment” (interview 2010-­10-­07). The ability to modulate one’s accessibility became a recurring trope. For example, in individual offices, glass walls were designed to serve both as windows inviting collaboration and as barriers enforcing solitude. In the architect’s words, “we took a frosting on the glass. When you’re sitting down, you’re visually disconnected. When you’re standing up, you’re visually connected” (interview 2010-­10-­07). While the architects and the building committee were advancing their own recipe for intellectual innovation, the decisions about who would actually do the interdisciplinary work were being shaped by the organizational practices of established public and private institutes. The search and hiring process was split into two different tracks, tied to the different institutional and funding orientations of the public university side (WID) and the private not-­for-­profit side (MIR). The former was shaped (though not determined) by an open and competitive process that the organizers referred to as “bottom-­up,” while the latter was driven by an entirely internal, nontransparent selection procedure that the organizers referred to as “top-­down.” Although much of the rhetoric around the two institutes focused on the potential for synergy between them, the two processes were kept entirely separate, as if synergy required an initial period of separate evolution. According to the first MIR director, “we don’t want to contaminate the [bottom]-­up process with the top-­down process” (interview 2010-­05-­24). On the WID side, the process was explicitly modeled on a previous interdisciplinary “Cluster Hire” program that Wisconsin created about a decade earlier (interview 2010-­05-­26; Phillips 2015; UW–­Madison 2008). In that effort, a total

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of fifteen million dollars in funding was assembled from a combination of public and private sources to hire over one hundred faculty members across campus in “clusters” of two to four, each focused on a particular interdisciplinary area, from materials science to upper Midwest folklore. Similarly, in choosing WID faculty, the organizers held a campus-­wide competition to select five “themes” that would populate the building. As one successful theme leader put it, “this is something unique to Wisconsin. I don’t know many places where you could actually write a proposal and have a chance to be in a brand-­new building and develop a program that no one from the top is saying ‘This is what you’re going to do’” (interview 2010-­04-­22). A successful theme proposal had to include several existing faculty and staff from campus; however, only the faculty leader from each theme moved into the Discovery Building. Each faculty leader then hired four new researchers into their group; these newcomers also inhabited the Discovery Building, but had their tenure homes in traditional academic departments and disciplines housed in other buildings across campus. On the MIR side, the process was different. The language of “themes” was replaced by the similar-­sounding “teams,” but the actual similarity was limited: the selection of faculty was made by the MIR leadership in consultation with the outside funding partner WARF, with no public input or competitive process. At this stage in the development of the initiative, whether the social metaphor was team or theme, the essence of interdisciplinarity for those involved centered around collaboration among the resulting researchers. Interdisciplinary practice amounted to researcher collaboration. As faculty were selected from around the UW–­Madison campus to lead the WID themes, and new faculty started to trickle in, the intellectual demographics of the Discovery Institutes revealed several, usually implicit, theories about the nature and social scope of interdisciplinary work. While a history of crossing disciplinary boundaries and a professional identity built on some idea of field blending was common to many of the chosen Discovery Institute team and theme leaders, interviews revealed no single understanding of “interdisciplinarity”—­ what it is, how it should be achieved, at what scale it operates, and what ends it serves—­among the institute occupants. Instead, we heard stories that combined highly contingent individual experiences with descriptions of specific social norms and practices in particular disciplines and interdisciplines. These characterizations cut across every scale, revealing how perceptions of the intellectual innovation of interdisciplinarity interacted with the organizational and physical innovations of the nascent institutes. Many of the team and theme leaders described their own relationship to interdisciplinarity in terms of their professional identities—­presenting interdisciplinarity as a intellectual and social property of particular people that can be discerned from their history of shifting between or combining fields. Thus, in

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describing how he came to be affiliated with an explicitly interdisciplinary institute, one theme leader in the WID, focused on epigenetics, noted that he was a biochemistry undergraduate, before complementing that with doctoral training in biophysics, and then doing “a postdoctoral training at University of Michigan Medical School, again in biological chemistry, which is sort of biochemistry” (interview 2010-­04-­22). Similarly, a researcher in a different theme designed around optimization research, who had completed a PhD in mathematics, held UW–­Madison departmental positions not in that field, but in both computer science and industrial and systems engineering before joining the WID group (interview 2011-­08-­24). Such interdisciplinary biographies occasionally extended beyond scientific disciplines: the leader of the WID theme centered around systems biology recalled, “actually, my first degree was in the humanities, ‘Great Books’ program at Columbia College” (interview 2010-­04-­22). Such biographies also spanned professional territories that were not neatly disciplinary in nature. Thus, the leader of the WID theme focused on simulated home health environments originally received a “baccalaureate in nursing . . . a master’s in psychiatric . . . I was going to be a psychiatric nurse.” The interdisciplinary potential of computer technology was a common notion among the Discovery Institutes theme and team leaders. The leader of the team in the MIR focused on medical devices began a career “as a clinical medical physicist . . . in a radiation therapy clinic” but “now, my work is probably algorithm design, so designing algorithms, and conceptualizing hardware and software systems” (interview 2010-­04-­20). Intriguingly, the potential for interdisciplinarity was occasionally even assigned by WID scientists to students, who could not be said to have interdisciplinary biographies just yet. Several Discovery Building theme and team leaders talked about selecting students on the basis of their ability and willingness to collaborate in such an environment. One WID theme leader noted “I select students based on how dyna[mic] . . . how interactive they are with other people” (interview 2010-­04-­22). Although many of the scientists we interviewed spoke of interdisciplinarity in biographical terms, the early development of the Institutes also revealed a commitment to small groups as a core unit of interdisciplinary work. The interdisciplinary potential of a research group was sometimes portrayed as a delicate, emergent social relation that could be spoiled by heavy-­handed leadership. As one faculty member involved in the WID selection process put it, “it is an art to developing interdisciplinary research groups, and we needed people who did that and people who weren’t completely dominating personalities who would drive people away or destroy group ethics or whatever” (interview 2010-­08-­06). More often, however, a group was presented as interdisciplinary purely on the basis of its intellectually diverse membership. Theme leaders (WID) and team

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leaders (MIR) were given relatively free rein to decide how their groups would be put together, and there was no uniform strategy for achieving intellectual diversity. The theme leader of the systems biology group created a diverse, multidepartmental search committee in order to hire a diverse, multidisciplinary team whose members found academic homes across several departments (interview 2011-­07-­15). By contrast, the first two hires on the theme focused on nanotechnology techniques for biological tissue engineering both entered the same department, Biomedical Engineering, even though they had field-­crossing backgrounds themselves (“a material scientist [doing] research in synthesizing nanoscale particles and drugs” and “an expert in studying the cell material interaction . . . focusing on stem cell bioprocessing and also regenerative biomaterials”) (interview 2011-­10-­31). In addition to the interdisciplinary biographies of the scientists and the diverse nature of the teams and themes, various stakeholders also discussed interdisciplinarity as a spatial building-­or institute-­wide phenomenon, emerging from collaborations across groups and even institutes. As one faculty researcher put it, “I don’t think there is anybody in this building who is not open to collaboration. Because why would you be in this building if you weren’t?” (interview 2014-­04-­09). The leader of the theme focused on systems biology elaborated, “this is something that I think, really, the campus here doesn’t emphasize yet quite so much, but it has all the elements: you’ve got the excellent biology; you’ve got great computational strengths; you’ve got people like myself who are trying to express the biology in mathematical or computational terms. Why don’t we try to build something up here?” (interview 2010-­04-­22). In the eyes of the designers, and of some scientists, the power of the Discovery Institutes themes and teams to catalyze interdisciplinarity would not be limited by walls of the building. Instead, the themes were sometimes framed as magnets for interdisciplinary collaboration, drawing colleagues from across campus into short-­term collaborations and conversations sited at the Discovery Building, whether in formal research conferences held in the main auditorium, or informal conversations in the café. As one of the WID theme faculty put it, “our other expectation was that it would sort of super charge our interdisciplinary collaborations with other people on campus who were not in the building by the fact that we would have more space for our group and that we would have this very nice location that’s very centrally located as far as engineering science part of campus is concerned, and that it would be a very convenient place for people to come and meet” (interview 2011-­08-­24). The rhetoric of “super charged” interdisciplinarity associated with the themes and teams had a certain missionary zeal that neatly intersected with the high ideals of the building’s architects. The idea of “Discovery at the Crossroads,” as the project manager put it, showed up early in the design process: “We create this

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pathway, a destination on the ground floor open to the public. Well, this became a pretty powerful concept that drove putting together the building” (interview 2010-­08-­18). The campus architect used the metaphor of a “Grand Central Station” to explain the goal: “a meeting point, a place that people needed to come to, but it was also a place that they traversed in going somewhere else” (interview 2012-­05-­03). One theme leader used similar terms to describe the effect of the themes that occupied the station, speaking hopefully about their “ability to bring those faculty to campus [who] will act as a catalyst . . . and as a hub for the rest of campus to be interested in epigenetics” (interview 2010-­04-­22). Some of this wider-­scale social interdisciplinarity was meant to be enhanced through conferences, workshops, and seminars held in Discovery Building common spaces. The leader of the MIR virology-­focused team likened it to “kind of hunting and gathering efforts, like these kinds of interdisciplinary seminars that would try to root out such opportunities, specific opportunities, and create environments in which groups of people on campus could reasonably suggest to each other: Well, you and you should get together on such-­and-­such problem” (interview 2010-­04-­14). For some groups with whom we spoke, such events worked very well. However, the theme and team researchers seemed to understand that instrumental goals lurked behind the enthusiasm for serendipitous collisions. One team leader on the MIR side explained how this might work: “for example, using [an] interdisciplinary seminar series . . . to, again, specifically identify: Oh, here’s a group of a half-­a-­dozen investigators that might be able to get together and do something really exciting and really important. They might have had some dim awareness of that previously, but they’re all very busy folks with many things pulling them in many different directions. . . . What’s needed to generate a multi-­investigator grant proposal, which might be harder to organize within an individual department if this is something that’s pulling people together from, for example, multiple schools” (interview 2010-­04-­14). Tracing the idea of interdisciplinarity through the early imaginings of the Discovery Institutes reveals a certain litheness of the concept that was deployed in a range of ways in decisions ranging from the location of bathrooms to the hiring of scientists. Physical and intellectual innovations intertwined in the plans of architects who offered idealistic images of scientists-­as-­particles swirling around and colliding in the light-­filled atrium and bustling café. Intellectual and organizational innovations flowed together as the scientists applied different implicit theories of interdisciplinary work to the selection of their teams. In both the spatial realization of the Discovery Building and the social realization of the Discovery Institutes housed within, ideas about interdisciplinarity were invoked at multiple scales by different actors. The architects saw interdisciplinarity as guiding the crossroads-­like arrangement of ground floor space, but also the choice of glass for partitions that would enable scientists (in theory) to move

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from collaborative space to private space simply by sitting down. The scientists found interdisciplinarity in their personal journeys from one school and one job to the next, but also in the meshing of people in a group and the critical mass of groups in a building.

The Frictions of Interdisciplinarity As the program and building committees went about their work, they shared the mostly unspoken principle that interdisciplinary work relies on interaction, and that both the organization and physical home of the Discovery Institutes should increase the likelihood and frequency of interaction between researchers from different disciplinary backgrounds. As the actual researchers began to fill out the teams and themes, and to inhabit the building, conflicts emerged between their existing practices—­their established notions of interaction—­and the organizational and physical structures of the Discovery Institutes, which embodied the program and building committees’ commitment to interaction. The seamless intertwining of innovations that characterized the design phase emerged as more complex and difficult in practice, and an additional set of stresses arose at the interface between new structures and older practices and norms. In keeping with the spatial and social metaphors for interdisciplinarity that emerged during the design phase, we term this recurring negotiation over the meaning of interdisciplinarity in practice as the “frictions” of interdisciplinarity. Others before us have observed the awkward encounters between the ambitious social engineering of architects and the daily realities of scientific life, describing scientists who taped foil over windows to protect light-­sensitive instruments or blunted the graceful curve of a wall with standard-­issue rectangular lab benches (Leslie 2008). In this case, the architects’ attempts to design the new Discovery Building for interaction strained spatial norms that scientists viewed as effective. For example, the leader of the epigenetics research theme noted that “the students and the postdocs’ desks are not adjacent to the lab bench, which they are now [in the old disciplinary building]. Instead, they’re going to be out in the open space, in these cubbies or carrels, or whatever they’re called, in the WID space.” Although this lead scientist respected the intent of the architects to resist the “silo-­building” of disciplinary science and “have graduate students next to other graduate students from different labs,” he saw something that the architects didn’t: the need of an experimenter to monitor her experiment in progress, to “actually see their experiments or see if something’s on fire or falls” (interview 2010-­04-­22). The subsequent compromise was to shuffle desk assignments so students could at least peer through the glass laboratory walls to see if their bench work was melting down, while still perhaps chatting with the adjacent graduate student from a different lab.

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For some more senior researchers, the Discovery Building’s emphasis on collaborative spaces proved both useful and problematic. The leader of the optimization research theme was quick to point out the disadvantages of his old lab in the computer science building, where “we did have meetings there with collaborators, but it would always be hard to find space and offices are fairly small” (interview 2011-­08-­24). Yet months after moving into the Discovery Building, he still returned to his old office, seeking a refuge from the collaborative atmosphere that frosted glass alone could not provide. In his words, “I am grateful that I can go back to my computer science office, which is just across the street, and have a more private, possibly quieter space to work in” (interview 2011-­08-­24). If certain features of the Discovery Building seemed like a sort of architectural overreach to the scientists inhabiting the building, there were other ways in which they appeared insufficient, on their own, to instigate the sort of interactions that the architects envisioned. One member of the tissue scaffolding theme praised the casual interactions prompted by the proximity to other labs, explaining that he would “run into senior scientists or graduate students from the lab and say hello, hear about what they’re doing if I just need a break. I think those interactions are invaluable” (interview 2014-­04-­09). At the same time, he observed that this effect seemed to apply only to labs on the same floor. Because the themes were assigned to specific floors, this comment suggests that interaction across themes was less common than the architects and planners envisioned. Within a year, a host of social mechanisms sprang up in a tacit admission that the established structures did not guarantee the types of interactions the Discovery Building creators imagined. As one scientist put it, “interdisciplinary work doesn’t happen by putting a bunch of people with different disciplines in the same room” (interview 2010-­06-­01). Many of these new social mechanisms centered around refreshments. A daily Tea at Three was created to entice faculty, staff, and students to mingle and converse over free food. Friday lunches in the researcher-­only cafeteria were priced to encourage mixing across the two institutes: “it will cost eight dollars, but if you bring somebody from the other Institute it’s only seven dollars” (interview 2013-­08-­28). And, for a time, the WID and MIR directors themselves hosted a Wednesday morning coffee. Scientists reported that these mechanisms were a qualified success. The head of the simulated health environments theme called the various teas and coffees “a good step,” but cautioned that one year after the opening of the building, there was still “no real cross collaboration in terms of extensive engagement between my group and other groups” (interview 2011-­07-­12). More than one scientist expressed optimism that social gatherings might help the labs connect through their students, even when faculty tended not to be regular participants. In the words of a new faculty member, “three o’clock is kind of when I’m trying to finish

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up to go home, so a lot of the time you don’t get a lot of the [principal investigators] at Tea at Three. A lot of the students go, which helps to foster interactions” (interview 2014-­04-­09). The notable prominence of students in these examples makes clear that, despite the elaborate process built up around the selection of new faculty, Discovery Building faculty saw much of the interdisciplinary activity that was supposed to take place in the Building as mediated by the activities of graduate students and postdoctoral fellows—­scientific workers who might be affiliated with one WID theme or MIR team, but who likely share space, equipment, and experimental goals with counterparts on other themes or teams. This has been observed elsewhere. Hackett and Rhoten (2009), in particular, pointed to the role of graduate students as connectors in interdisciplinary networks. Relatively little of the planning for the Discovery Institutes focused on this mechanism of interdisciplinarity—­or on the details of the academic division of labor in general. Interestingly, interdisciplinary value was an assumed property of the social networks of high-­profile interdisciplinary researchers, which would follow those researchers to the Institutes as long as they were spatially collocated. In general, scientists’ characterizations of the early successes and failures of the Discovery Building point to the imperfect alignment of intellectual, organizational, and material innovations, as they were conceptualized, and the perhaps inevitable need for the development of social mechanisms to facilitate the hoped-­for spatial interdisciplinarity. They also point to subtle mismatches between the ideas of interdisciplinarity that animated the vision of the architects (who focused on material innovations) and those of the scientists who came to inhabit the building (who were more likely to intertwine the intellectual, organizational, and material). For example, the need for a faculty member to physically leave the building in search of calm suggests subtle disagreements about the appropriate balance between open, collaborative space and focused, isolated space. In this context, it is interesting to consider the ramifications of scientists retreating from an ostensibly interdisciplinary space, both literally and metaphorically. Although a researcher’s desire to seek refuge in the quieter confines of his or her old departmental office can be read in purely practical terms, it also suggests a need to temper one’s participation in the interdisciplinary atmosphere of the new institute—­an instinct that interdisciplinary is good when taken in the right dose. In effect, interdisciplinarity is being constructed by these researchers as having a particular temporality as well as an ideal spatiality. That same computer scientist’s desire to retreat to a previous departmental office for a respite from interdisciplinarity points to a more painful site of tension around the new interdisciplinary institutes: the dual affiliations of WID faculty, all of whom were also appointed as tenured or tenure-­track faculty members in a traditional departments centered elsewhere on campus. Every

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principal investigator WID faculty member we interviewed talked about the tension between increased (material and intellectual) proximity to their new interdisciplinary colleagues in the WID and increased distance from their disciplinary colleagues in their home departments. Of course, multiple affiliations (and multiple offices) were not new to many of the WID-­affiliated scientists. For example, the simulated health environments theme leader had offices in the School of Nursing and the College of Engineering before affiliating with WID and reported that “many times I am in two or three buildings in the same day” (interview 2011-­07-­12). Prior experience with split academic identity did not necessarily make the tensions easy, but it did mean that the lead scientists knew what they were getting into. The leader of the MIR team focused on core computational technology noted succinctly that moving to the institutes was “a trade-­off, you know. It will create stress. . . . Are you here? Are you there? Are you in between? Where are you?” (interview 2010-­04-­26). Even before the Discovery Building opened, the leader of the systems biology theme worried that “by building more connections with others, that maybe I lose some connections with those in the department” (interview 2010-­04-­22). The challenge of balancing affiliations had a different emotional weight, and perhaps different practical implications, for junior and senior personnel at the Discovery Institutes. For the senior scholars, becoming part of the new venture was less a risk of entering a new interdisciplinary environment, and more a validation of previous interdisciplinary work they had already produced. As one MIR team leader put it: “I’m a person of a certain age, sixty-­one, and I am ready for one more big challenge. And [the MIR leader] basically sold me on the idea that this was a place where big visions and high-­risk, potential high-­pay-­off kind of work can be done” (interview 2010-­04-­14). For faculty and researchers at this level, the frictions between department and institute manifested in the increased administrative burden—­what might be called the administrative overhead of interdisciplinarity. As one WID theme leader told us, “this is basically volunteer work . . . for all of us, this has been on top of everything that we already do” (interview 2010-­06-­01). The new administrative work began as soon as the new tenants of the Discovery building were announced. Even the WID theme leader who had “had a joint appointment for fifteen years” felt overwhelmed like never before: “we immediately had very complicated jobs to start: recruiting for new people, making architectural decisions about the building, making policy decisions about the building, and we had none of our time devoted to our work here. So, we all have other jobs and so this became a job on top of another job. So I was a department chair, I run a ten-­million-­dollar research program, I teach. So, this was something I barely could squeeze out a couple hours for” (interview 2011-­07-­12). These trade-­offs were real and painful for many.

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On the other hand, junior faculty hired to work at the Discovery Institutes viewed the department-­or-­institutes trade-­off as a series of choices that were inconvenient at best and professionally harmful at worst. As one newly hired assistant professor put it, “there’s direct conflicts between meetings that I have for the department and meetings that happen here at WID. Seminars there and seminars here, um, grants that I write either through here that get overhead, or overhead that goes to the department. So, there’s always a choice to be made. And some of those, I think, choices, should not be choices . . . they should not be something I have to think about every single time I go through my daily existence” (interview 2012-­11-­09). Another newly hired assistant professor pointed out that junior faculty had a disincentive to collaborate, and that the prevalence of junior faculty in the Discovery Building could actually magnify that. In his words, “it takes time for . . . research to build, especially when the majority of faculty on the WID side would be junior faculty—­so they’re just getting in and trying to build a research program . . . there is a natural push to get grants based solely off of your own research [as against research as part of an interdisciplinary team], so that you can say this is what I’m contributing because you have to run up for tenure” (interview 2014-­04-­09). The leaders of the Discovery Institutes were aware of many of these tensions. The head of one MIR team noted, succinctly, that “someone who is looking for tenure or other kinds of necessary survival-­linked issues may face a disincentive to get involved in some kinds of interdisciplinary interaction” (interview 2010-­04-­14). And the initial interim director of MIR acknowledged that “there are people who think that assistant professors shouldn’t be involved in interdisciplinary endeavors because . . . [they won’t have] as easy a time getting tenure as if they stay home [in their disciplines]” (interview 2010-­05-­26). On the other hand, the MIR director was also able to point to local examples where interdisciplinarity—­in the sense of multiple affiliations—­hadn’t disadvantaged junior faculty. Those who created and populated the Wisconsin Institutes for Discovery did not see the spatial and social efforts to carefully engineer interdisciplinarity—­ supposed “best practices” based on lessons brought together from the wisdom of past initiatives and the examples of competing research centers—­as sufficient to achieve success. In their descriptions, the layers of intellectual, organizational, and material innovation through which they moved on a daily basis did not piece together smoothly, and idealized mechanisms for facilitating interdisciplinarity confronted messy organizational and everyday realities that could not be simply brushed away.

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Conclusion Our assessment of the Wisconsin Institutes of Discovery leads us to argue for the value of thinking about interdisciplinarity not as an easily measured and assessed epistemological phenomena that simply involves blending and blurring established intellectual boundaries. Understanding interdisciplinarity at the Discovery Institutes means exploring the characterizations of its creators and participants across four dimensions: not just intellectual, but also financial, material, and organizational. Considering hybrid structures across each of these levels, as many of our respondents do, helps capture the complexity of interdisciplinarity as an idea rooted in both spatial and social metaphors, constantly open to reinterpretation and redeployment across the academic division of labor in such a research institute. In our case, we see that interdisciplinarity is a highly flexible notion, deployed as a strategic claim and critical resource by researchers and their advocates. It is not so much a product of intellectual, financial, organizational, and material components—­with a singular, transcontextual meaning supposedly easily reproduced and transplanted through “best-­practices” benchmarking—­but rather a process where what “counts” as either a cost or benefit of interdisciplinarity is articulated and negotiated by various actors again and again. Much research has pointed to the challenges of achieving radical interdisciplinarity at an intellectual level (e.g., Andreasen and Brown 2005). Our respondents describe the intellectual as inextricably connected to other features of the environment in which interdisciplinarity is defined. Their characterizations make clear we should not understand interdisciplinarity at the intellectual level alone, but should consider the ways in which constructed interdisciplinary meanings are deeply entwined with claims about building architecture, organizational factors, research practice, and, of course, funding. This intertwining leads scientists and stakeholders to understand the benefits and frictions of interdisciplinary through both spatial and social (and sometimes even temporal) metaphors—­ searching for productive collisions while negotiating inevitable frictions. At the same time, through loose coupling across the scales we describe, those involved have substantial flexibility in how they will act and ultimately what they will define as successful interdisciplinarity. In other words, the entwining that we capture suggests it is difficult to precisely name and assess a single stable form of interdisciplinarity, making it problematic to unequivocally measure success and failure. As of this writing, the Discovery Building is almost five years old, and nearly all the WID themes and MIR teams have made substantial progress on hiring to build out their research groups. While work in the building’s laboratories proceeds, the character and quality of interdisciplinarity are not

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fully settled. Both the WID side and the MIR side have had three different formal and interim directors. These directors have each promoted different visions. One research group focused on educational games that started on the MIR side under one faculty leader (as a research team) has since moved over to the WID side under a different faculty leader (as a research theme). And for many groups, the bureaucratic and budgetary issues of funding faculty through the departmental side (outside of the building) but funding grant projects through the research center side (within the building) still cause conflict. But even if the Discovery Institutes had achieved a greater measure of organizational stability, it is not clear that it would be possible to provide an assessment of its successes and shortcomings that all involved could agree on. Responses to the question “What would success look like at the Discovery Institutes?” have varied widely among faculty and staff who have been involved in the project. Some point to awards and prominence. Others pin success to research quality defined in varying ways or practical applications of that research. In lean budget times, simply being able to grow faculty, staff, student, and infrastructure resources is considered a measure of success by some. Others offer a more comprehensive measure of success asking whether the Institutes “triggered the dialogue and discussion, interaction among different research teams, and  .  .  . engaged and garnered the support of the general public, and also involved other researchers from the campus” (interview 2010-­05-­12). The case of the Discovery Institutes suggests that the dream of success through interdisciplinarity is difficult to permanently fix in place—­and perhaps that is precisely its mobilizing power. notes

This project would not have been possible without the participation of the many people involved in developing and working as part of the Wisconsin Institutes for Discovery. We thank all of them. In addition, we have benefited from feedback provided by scholars and WID stakeholders at the “Understanding Innovative Science” symposium we organized in Madison in the fall of 2014 and the comments we received on earlier drafts of this chapter from the editors of this volume. Finally, we are grateful for support for this project from the National Science Foundation (PRJ61JS), the Wisconsin Alumni Research Foundation, and the Office of the Vice Chancellor for Research and Graduate Education at the University of Wisconsin–­Madison (with support from WARF). Daniel Kleinman also received support from Korea Research Foundation (NRF-­2010-­330-­B00169). The views expressed here are those of the authors and do not necessarily reflect the views of those who provided funding. 1   In this chapter we have chosen to cite interviews anonymously, referenced by a unique date code. Much of our primary data will be made available to interested scholars as part of a digital archive at UW–­Madison. For further information, contact the authors.

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Albert, Mathieu, Elise Paradis, and Ayelet Kuper. 2015. “Interdisciplinary Promises Versus Practices in Medicine: The Decoupled Experiences of Social Sciences and Humanities Scholars.” Social Science & Medicine 126: 17–­25. Andreasen, Nancy, and Theodore L. Brown, eds. 2005. Facilitating Interdisciplinary Research. Washington, DC: National Academies Press. Apple, Rima D. 1989. “Patenting University Research: Harry Steenbock and the Wisconsin Alumni Research Foundation.” Isis 80: 375–­394. Devitt, Terry. 2004. “Wisconsin Poised to Invest $750 Million in Biomedical Research.” http://news.wisc.edu/wisconsin-­poised-­to-­invest-­750-­million-­in-­biomedical-­research/. Frodeman, Robert, and Carl Mitcham. 2007. “New Directions in Interdisciplinarity: Broad, Deep, and Critical.” Bulletin of Science, Technology & Society 27 (6): 506–­514. Gibbons, Michael, Camille Limoges, Helga Nowotny, Simon Schwartzman, Peter Scott, and Martin Trow. 1994. The New Production of Knowledge: The Dynamics of Science and Research in Contemporary Societies. Thousand Oaks, CA: Sage. Hackett, Edward J., and Diana R. Rhoten. 2009. “The Snowbird Charrette: Integrative Interdisciplinary Collaboration in Environmental Research Design.” Minerva 47 (4): 407–­440. Jacobs, Jerry A. 2013. In Defense of Disciplines: Interdisciplinarity and Specialization in the Research University. Chicago: University of Chicago Press. Jacobs, Jerry A., and Scott Frickel. 2009. “Interdisciplinarity: A Critical Assessment.” Annual Review of Sociology 35: 43–­65. Jeffrey, Paul. 2003. “Smoothing the Waters: Observations on the Process of Cross-­Disciplinary Research Collaboration.” Social Studies of Science 33 (4): 539–­562. Klein, Julie Thompson. 2010. Creating Interdisciplinary Campus Cultures: A Model for Strength and Sustainability. San Francisco: Jossey-­Bass. Kleinman, Daniel Lee, Noah Weeth Feinstein, and Greg Downey. 2013. “Beyond Commercialization: Science, Higher Education and the Culture of Neoliberalism.” Science & Education 22 (10): 2385–­2401. Leslie, Stuart W. 2008. “‘A Different Kind of Beauty’: Scientific and Architectural Style in I.M. Pei’s Mesa Laboratory and Louis Kahn’s Salk Institute.” Historical Studies in the Natural Sciences 38 (2): 173–­221. National Science Foundation. 2011. Rebuilding the Mosaic: Fostering Research in the Social, Behavioral, and Economic Sciences at the National Science Foundation in the Next Decade. NSF 11-­086. Arlington, VA: National Science Foundation. Phillips, Susan D. 2015. Faculty Cluster Hiring for Diversity and Institutional Climate. Washington, DC: Urban Universities for Health. Rhoten, Diana. 2004. “Interdisciplinary Research: Trend or Transition.” Items & Issues 5 (1–­ 2): 6–­11. Sá, Creso M. 2008. “‘Interdisciplinary Strategies’ in U.S. Research Universities.” Higher Education 55: 537–­552. University of Wisconsin–­Madison. 2008. “Report of the Cluster/Interdisciplinary Advisory Committee to Evaluate the Cluster Hiring Initiative.” Retrieved from http://clusters.wisc .edu/documents/ClusterReport_2008.pdf. Weingart, Peter, and Nico Stehr, eds. 2000. Practicing Interdisciplinarity. Toronto: University of Toronto Press. Wisconsin Institute for Discovery. 2014. “FAQ | Wisconsin Institute for Discovery.” http:// wid.wisc.edu/about/faq. Accessed August 22, 2014. Yaneva, Albena. 2010. “Architecture as a Type of Connector.” Perspecta 42: 138–­143.

3 ◆ Epistemic Cultures of Collaboration Coherence and Ambiguity in Interdisciplinarity L au r e l Smi t h - ­D o e r r , J e n n if e r C r o i s s a n t, I ta i Va r d i , a n d Tim ot h y S a c c o

In this chapter we draw upon Knorr Cetina’s (1999) epistemic cultures concept to analyze interdisciplinary collaboration. Epistemic cultures are the beliefs and practices that constitute attitudes toward and ways of warranting knowledge within a particular group or community. A key practice in the epistemic culture of the chemical sciences is interdisciplinary collaboration. Collaboration and interdisciplinarity in a certain sense presume one another: interdisciplinary work is generally assumed to require the coordination of several people from multiple fields. Conversely, the point of collaboration is generally understood to be the bringing together of diverse perspectives. Our interlocutors generally assumed that one implied the other, without much reflection. We collected data from four sites in the chemical sciences, including ten to twenty months of field observations in each site and 106 interviews total. The interviews reveal significant diversity encapsulated under the collaboration label, and hints of how chemical scientists think about interdisciplinarity show up early in our interviews when respondents are queried about their area of research. Their responses highlight the diversity of perspectives and orientations within the chemical sciences. No one claims to be a plain chemist. Instead, respondents identify with organic chemistry, inorganic chemistry, physical chemistry, biochemistry, or analytical chemistry. A number of respondents specialized in theoretical chemistry, and some even described their area as 65

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chemistry education research. Extensive talk about interdisciplinarity was evidenced in several registers. In this chapter we take a somewhat unorthodox approach to studying interdisciplinary collaboration based on the kind of access we had to our four lab sites. The four sites are (1) Dr. Michelle’s traditional chemistry lab, (2) Dr. Paul’s chemistry lab established after his return to academia from industry, (3) Dr. Brian’s lab, a hub of a large interdisciplinary center charged with creating applications to chemical science based products, and (4) Michael’s group conducting chemical analyses in a start-­up biotech firm. We observed regular weekly, within-­ group lab meetings and conducted interviews with lab members. We did not observe conferences or lab sessions that our respondents had with their outside, interdisciplinary collaborators. And yet the lab members often talked about, joked about, and told stories of interdisciplinary collaboration, and raised issues of credit, funding, and careerism that swirled around the issues of interdisciplinary collaboration. Because what is considered interdisciplinary is a matter of conceptualization and contingent on the standards used to delineate one discipline from another (Timmermans and Epstein 2010), we allow the definition of interdisciplinarity to emerge from our data.

Defining Interdisciplinary Collaboration in Context Our approach to interdisciplinarity closely follows our interlocutors’ meanings of interdisciplinary collaboration. For some of them interdisciplinary collaboration means across groups within the chemical sciences (e.g., inorganic and organic chemistry); for others it means between traditional disciplines (e.g., chemistry and biology). In this section, to illustrate what our respondents mean by interdisciplinary collaboration, we provide one example from each of our four lab settings that demonstrates the working definition that scientists use. These definitions are a starting point. We argue later that the different lab settings produce variation in the ambiguities around interdisciplinary collaboration and in how chemical scientists repair that ambiguity, maintaining the desirability of interdisciplinary collaboration. In the traditional academic chemistry lab, where Dr. Michelle is the principal investigator (PI), the lab members talk about their inorganic chemistry lab needing to form cross-­disciplinary collaboration with crystallographers.1 While both are subfields of the chemical sciences, the members discuss the collaboration as crossing disciplines. For example, when asked in an interview about interdisciplinary collaboration, one graduate student discusses how her samples were sent to a new crystallographer who had necessary equipment and the collaborative relationship is off to a slow start:

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After one of the second reviews that came back from my first author paper, one of the readers requested added data. And we don’t have access to that type of equipment, so Dr. Michelle had been in contact with a colleague of hers [at another institution] that has this facility and has collected data for other colleagues in the past. So she suggested sending out samples to this PI. So yeah, I made samples, documented everything for this person, because he’s just collecting data on this compound. So I sent that out and still ­haven’t heard back. I mean, maybe we should be pushing more, but we know that he’s got no students and he’s got a heavy teaching load on his side, so my samples are there [pauses]. I have faith that we’ll get it at some point.

One interpretation of this student’s complaint is that the collaborator is located far away in a geographic sense, a kind of distance that seems to converge with the collaborator’s disciplinary distance. And yet, we heard the lab members discuss the same kinds of issues in managing disciplinary distance with a crystallographer who was in another unit at the same university. In the end, Dr.  Michelle’s lab went with the faraway collaborator because he did it for the data access while the local crystallographer “charged too much.” The interdisciplinary definition in Dr. Michelle’s lab seemed to follow the collaborations where clear monetized (or new data access) benefits were required to make the cross-­disciplinary work happen. In the traditional lab, these are often literally instrumental collaborations. The lab led by Dr. Paul, who had had a stint in industry and returned to academia, seems to have a more conventional take on interdisciplinary collaboration. Dr. Paul was from industry, where interdisciplinary collaboration is often built into the structure of the firms—­with members of discipline-­based departments working in cross-­department teams for health targets. Perhaps this experience is related to why his lab seems clearer than Dr. Michelle’s group on which of Dr. Paul’s projects are interdisciplinary. When asked to describe interdisciplinary collaboration, Dr. Paul said, “A good collaboration: I can use as an example this five PI project that we have running. Among those five PIs, there are three from chemistry, one from bioengineering, and one from biology.” In contrast to Dr. Michelle and her group, Dr. Paul lumps the chemical sciences labs into the “chemistry” category, rather than distinguishing between subdisciplines in the chemical sciences. In the first two sites—­Dr. Michelle’s and Dr. Paul’s labs—­we can see some of the range in how interdisciplinary collaboration is defined. Our third site—­Dr.  Brian’s lab—­is the core of an interdisciplinary center funded by a large collaborative grant. One of the co-­PIs described the interdisciplinary collaboration as allowing a sharing of resources that was unusual in academia. This chemistry co-­PI said in an interview,

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A good collaboration is where people keep their eyes on what the team is trying to accomplish and the resources that are used to achieve goals independent of where the monies were allocated initially. A good example would be perhaps we only needed half the money to make a compound than we initially thought we could. So if the next phase of study involves biology and you can use the additional monies to support more biological work, then you want to slide the money over and do the work that will advance the goals of the group. That kind of collaboration usually comes up with interesting results or at least a nice collegial environment where people are really trying to work toward a set of shared goals. And usually, those kinds of things are productive, and in this very difficult time for funding there is a better chance that you are going to get people to let you continue your work if you’ve worked well together. And that’s a joy.

In contrast to the traditional academic lab where interdisciplinary collaboration means spending resources in a clear division of labor, this description of the interdisciplinary research center views collaborators as sharing resources within the boundaries of the project but across traditional disciplinary lines like chemistry and biology. Interdisciplinary collaboration is defined here in a variety of ways. At times, it meant the traditional disciplines working together (as in the quote), while other times it resembled working across subdisciplines within chemical sciences. These variations in defining collaboration across our three academic sites are subtle indicators of the local contexts of the epistemic culture of the chemical sciences. In the industry firm lab, interdisciplinary collaboration clearly fell along the lines of the cross-­unit coordination within the company. The lab members talked about the functions of the firm within the discipline-­based groups, and how they worked with the other groups. The chemical sciences group that we observed conducted tests that other units in the company rely upon. One of the bench scientists describes the difficulty in balancing the demands for their tests by the other units—­a basic tension in the kind of interdisciplinary collaboration in which the scientists working in the firm see themselves engaged: Now, we tell them [scientists in other units] if you give us the samples Monday you can have them, the results, fully fleshed out by Friday, kind of thing. Or we’re really backed up. People have been really good when we say we’re swamped, we’re dying, or we can only get you the samples in three weeks. People are usually like “okay that’s fine.” Or if they have really important samples, like “I really need these done,” they’ll go and talk to other people [in other units] . . . to ask to have them push [their] sample down. So I think we’re pretty communicative around here about stuff like that.

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The scheduling of the cross-­disciplinary work is a key point of discussion and tension in the industry lab. As in the traditional academic lab, in the industry lab we often saw instrument-­based interdisciplinary collaboration. The importance of organizational context to articulation and variation in broad practices within epistemic culture can be seen in how interdisciplinarity generates different kinds of ambiguity in different settings in the chemical sciences. Ultimately a key contribution of this chapter is to refine the concept of epistemic culture to show how knowledge beliefs and practices are locally instantiated within different organizational contexts. Interdisciplinary collaboration is a central practice to the epistemic culture of the chemical sciences, and has coherent and positive narratives across the discipline while locally situated in specific organizations.

Research on Interdisciplinary Collaboration Interdisciplinarity has become a frequent object of study in sociology ( Jacobs and Frickel 2009). One central reason for the growing scholarship on interdisciplinarity may be the increasing application and commercial focus guiding research agendas (Berman 2012; Powell and Snellman 2004; Slaughter and Leslie 1997) and a wide acceptance of the interdisciplinary collaboration model as a way to achieve commercial outcomes (Gibbons et al. 1994; Nowotny et al. 2001; Brint 2009). Scholars often examine the tensions and challenges involved in the crossing of disciplinary boundaries. Writing aimed at more popular audiences usually takes a normative stance and either criticizes interdisciplinarity ( Jacobs 2013) or advocates for it (Klein 2010). Other works build on the process-­based conceptualization of collaboration. Lamont et al. (2012) argue that by understanding mutually constitutive social, emotional, and cognitive dynamics, scholarship can identify common dimensions found across a range of interdisciplinary contexts. Centellas et al. (2013, 312) use the term “calibration” to describe how participants align “different disciplinary identities and interests around a particular problem but without necessarily blurring or softening disciplinary identities or local meanings.” They found that strict disciplinary boundaries provide structure within which collaborators can operate. Our approach employs a disciplinary concept—­epistemic culture—­to investigate interdisciplinary collaborations in the chemical sciences. We use this innovative focus to compare different laboratory contexts. Albert et al. (2008) note that the epistemic culture concept, while useful for thinking about practices within and between disciplinary related cultures, misses more structural elements of interdisciplinary tension such as power. While we follow Albert et al.

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in expanding the structural aspects of Knorr Cetina’s (1999) epistemic culture concept in our analyses, rather than taking a macro focus on the field level we examine variation at the organizational level of laboratory life. On the whole, the prior literature examines interdisciplinarity within academic settings, and rarely takes a comparative look at the broader organizational contexts where interdisciplinarity occurs—­including industrial science (cf. Rabinow 1996; Rabinow and Dan-­Cohen 2006). Research tends to focus on familiar academic spaces such as peer review (Lamont 2009; Albert et al. 2008), conferences, and laboratory settings. Parker and Hackett (2012) usefully compare contexts by examining the subjective and emotional experiences of standard academic settings to small retreat settings where “island time” operates. The ways that an academic setting shapes interdisciplinarity, however, may be difficult to ascertain when there is no comparison to nonacademic contexts. There are logistical reasons for the dearth of comparative studies. The difficulty of access for qualitative, in-­depth studies of industry settings may be one reason why there are so few studies outside of academia. Indeed, our own attempts to gain access to study multiple industry sites were met with limited success, as described in the data and methods section. Yet as Frickel and Moore (2006, 12) emphasize, contemporary science is performed over a multitude of organizational types, hybrids, and settings. Focusing solely on academia neglects the variety of organizational contexts interdisciplinarity has emerged in, and therefore lacks an understanding of how context shapes interdisciplinary collaboration. Jacobs and Frickel (2009) conclude in their review that there is a need for comparative research designs to better understand interdisciplinarity beyond the narrow light recent scholarship has shed on this phenomenon. Previous conceptual work on interdisciplinary collaboration has emphasized definitions, such as variations between interdisciplinarity, multidisciplinarity, and transdisciplinarity (e.g., Pfirman and Martin 2010), and empirical work often discusses quantitative measurement of coauthorship or citation measures of interdisciplinarity (e.g., Leahey and Reikowsky 2008; Porter and Rafols 2009). We see a need for both more theoretical work and more empirical work on interdisciplinary collaboration. To do so in this chapter we connect the theoretical concept of epistemic culture to our empirical focus on variation in contexts in which interdisciplinary collaboration is interpreted and enacted.

Organizational Contexts of Interdisciplinary Collaboration While recent qualitative studies have revealed some of the complexity of interdisciplinarity (Parker and Hackett 2012; Lamont 2009; Albert et al. 2008), not

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enough is yet known about how interdisciplinary collaboration is perceived and managed by scientists in different organizational contexts. Two key kinds of scientific contexts are academic and industry contexts of science, which have been contrasted in the sociology of science since Merton described them in the early postwar period. More recently, sociologists have noted the overlapping and paradoxical changes in both academic and industry science (e.g., Kleinman and Vallas 2001; Berman 2013; Smith-­Doerr 2005; Owen-­Smith 2005; Vallas and Kleinman 2008; Hackett 1990). Rather than focusing narrowly on the academic sector, this line of scholarship relies on commercial industry contexts to gauge comparative developments in academic science. We employ a similar approach to understand how academic and industry context shapes variation around the core practice of interdisciplinary collaboration in the chemical sciences.

Epistemic Culture and Interdisciplinary Collaboration Karin Knorr Cetina conceptualized the phrase epistemic culture to articulate how knowledge production is built on practices, arrangements, and mechanisms. Knorr Cetina writes, “The notion of epistemic culture is designed to capture these interiorized processes of knowledge creation. It refers to those sets of practices, arrangements and mechanisms bound together by necessity, affinity, and historical coincidence which, in a given area of professional expertise, make up how we know what we know. Epistemic cultures are cultures of creating and warranting knowledge” (2007, 363, emphasis added). Knorr Cetina (1999) argues that there is not a single, static cultural structure guiding knowledge production; rather, there are many, with each scientific discipline having its own set of norms and customs associated with the knowledge being produced, the relationship between the researcher and the objects being studied, and the nature of scientific inquiry unique to each discipline. She investigated two very different fields of science: high-­energy physics and molecular biology. Knowledge in those fields was warranted in quite different locations, for example, in particle accelerators versus animal models. The chemical sciences, the subject of our analysis, include wide ranging subdisciplinary areas that many of our interlocutors understand as the basis for interdisciplinary collaboration. According to the American Chemical Society (ACS), the largest divisions in the chemical sciences are analytical chemistry, biological chemistry, chemical education, environmental chemistry, inorganic chemistry, medicinal chemistry, organic chemistry, physical chemistry, and polymeric materials (ACS n.d.). In contrast to Knorr Cetina, we look at variation within one epistemic culture—­chemical sciences—­although it is an epistemic culture that has a wide variety of large subdisciplines. The ACS has over 150,000 members, and each of the largest divisions has more than 3,500 members each. Thus

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in terms of sheer size, the fact that the chemical sciences seem like an “interdisciplinary” discipline to some of our respondents is perhaps not surprising. Thus, while epistemic culture is a very useful concept for this study of interdisciplinary collaboration in the chemical sciences, there was an analytic gap for us particularly in how to explain the similarity in talk (i.e., interdisciplinarity’s description as important) at the same time as the differences in interdisciplinarity’s meanings and practices. For bridging this gap in our analysis, we found Griswold’s (1987) discussion of legitimate objects as both coherent and ambiguous to be very useful. Griswold’s empirical context is quite different (how reviewers of different nationalities interpret novels about colonialism), and yet her analysis of powerful cultural objects as simultaneously coherent and ambiguous is helpful for us in understanding how chemical scientists in different contexts view interdisciplinarity. For her, the coherence was demonstrated by the diverse readerships all agreeing that novels by an author named Lamming were important. The ambiguity was in discussion of why Lamming’s novels were important. In Griswold’s study, the same novels analyzed by readers from the West Indies, Britain, and the United States took on meanings that emphasized in turn national identity, literary character, and race. The capacity of these powerful cultural objects to take on multiple meanings, while still retaining coherence, is shown in her study. For Griswold, national contexts exert influence on meaning; in our study, lab organization contexts (from academic to industrial) exert influence on the meaning of interdisciplinary collaboration.

Data and Methods The data come from observations made from 2010 to 2013 of four chemical science laboratories, and interviews with scientists mostly working in those labs. The labs fall along a spectrum—­from pure academic to pure industry. The first laboratory, led by Dr. Michelle, is a traditional academic chemistry lab in a private research university. The second academic laboratory is led by Dr. Paul, who recently entered a private research university from a career in industry. The third laboratory, led by Dr. Brian, is located at a public research university and is the lead lab for a large interdisciplinary research center charged with developing knowledge that can be applied to industrial products. The fourth laboratory is in a start-­up firm, in which Ben and Heather were the lab group leaders and the manager of the division, Michael, ran most the meetings observed. Across our four research sites, observation data were primarily collected at regular weekly lab meetings. Members of our research team did not participate in these meetings; rather, we observed the interactive dynamics of the lab members and focused observations on collaborations, which was the main goal of our larger project. Data were recorded in the form of field notes. Aside from

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recording observations, researchers also reflexively noted how their presence at the meetings might have influenced meeting dynamics. One important methodological consideration is that observations of meetings yield different results than observing other forms of scientific practice, such as bench work. Meetings are formally structured and somewhat predictable. Bench work is often a solitary act, whereas meetings are collective affairs; for some students lab meetings may be the most face time they get with their lab PI, depending on the nature of their own work. Therefore, drawing from Goffman (1959), the very nature of a lab can invoke a different “presentation of self ” than other forms of scientific practice. At these meetings members discussed what they were working on, both individually and collaboratively, what skills and expertise were needed or relevant to their research, and other issues related to the lab research agenda. A limitation for social scientists who want to interrogate interdisciplinarity in industry settings is that research in private-­sector settings often remains elusive. Our primary industrial site is the only one to which we successfully gained access. Calls to local firms and meetings with company representatives in the chemical and materials industries to establish a second site were unsuccessful. Reasons given for refusing access include lack of time to host a researcher and, more importantly, intellectual property and disclosure concerns. Despite our promises of anonymity and expression of disinterest in scientific details, no companies were willing to partner with social scientists without an internal champion in the company. In our experience, it takes specific social contact (personal ties) to gain entrée to private-­sector research groups. In addition to observations, eighty-­two scientists from across our four research sites were interviewed, some multiple times. These 106 interviews each took approximately forty-­five minutes to complete and focused on respondents’ professional histories in science and experiences with collaboration. We requested interviews with all members of the observed groups. Confidentiality was promised for all of our interview respondents and lab groups. Therefore, all names of individuals, organizations, and institutions are pseudonyms. A methodological issue that may be unique to qualitative study of interdisciplinary collaboration is the difficulty of managing access and confidentiality concerns. Frequently, qualitative lab studies in academic settings can pass institutional human subjects review by ensuring the participants that the name of the faculty PI and the individual members will be kept confidential in reporting the findings. We were required to include this condition in our research. The problem, however, is that in describing the interdisciplinary collaboration partners of a lab, it becomes more difficult to mask identities. People in the field are even more likely to be able to identify which member of their disciplinary area collaborates with a certain other subdiscipline. In this chapter we mask the identities of our respondents in accordance with the confidentiality agreement,

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but protecting identities does lead to barriers in our ability to report more specifically on the kinds of interdisciplinary collaboration that people at our sites engaged in. By investigating interdisciplinarity across the four sites, however, the power of comparative methods can be leveraged. While there have been steady calls for incorporating comparative methods into qualitative research designs (Hedström and Ylikoski 2010; Frickel and Moore 2006, 13), one major challenge is conducting time-­intensive observational methods in multiple sites. Our research strategy incorporates a team research design to help deal with this challenge. Team-­based research presents its own challenges and coordination issues. To aid in coordination, we used ATLAS.ti—­a qualitative data analysis software—­in coding and analyzing our data to construct the findings discussed later. In coding, the raw data were read from interview transcripts and field notes to develop initial codes about interdisciplinary collaboration (Emerson et al. 1995).

Findings We found interdisciplinary collaboration to be coherent and ambiguous (Griswold 1987), in both narratives and practice. We organize the presentation of our findings around (1) the places where the narratives about interdisciplinary collaboration were coherent and (2) where narratives were ambiguous across the laboratory settings, and then move to observations of ambiguities in practices of interdisciplinary collaboration—­(3) first in the academic labs and (4) then in the industry lab. Coherence in Narratives

The most common theme about interdisciplinary collaboration is its benefits. The instrumental argument provides a coherent meaning for interdisciplinary collaboration, one associated with positive outcomes for science. Especially in academic settings, scientists commonly used narratives about choice to talk about interdisciplinary collaboration. A frequent utilitarian choice narrative concerned choosing the best collaborators based on criteria such as expertise, institutional location, available equipment, or funding. Consider Dr. Angela, a research faculty member located at a public university. When asked what she looked for in a collaborator, Dr. Angela replied, “I’m interested in chemistry of disease, so we are then interested in making drugs. If you make a drug there is a procedure that you have to follow, and that procedure includes animal research  .  .  . there’s already people that have that expertise, and why would I duplicate that?” Dr. Angela’s summary of what she looks for in a collaborator is relatively simple—­someone who does what she does not do. This

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kind of narrative about how chemical scientists choose the most optimal division of labor for interdisciplinary collaboration was discussed by forty-­three of our eighty-­two interviewees. Across our four sites, researchers regularly described choosing collaborators based on how their expertise will strengthen a research project, and felt that working collectively on a project would yield mutual gains. As one of the scientists in the industry lab put it, “People understand that their individual work, the day-­to-­day work that they are doing, is only a part of a whole. And no one is going to be successful unless we’re all successful.” Similarly, an academic scientist described the correct approach to collaboration as “not being one-­sided, being receptive to [both] receiving and giving help.” Ambiguity in Narratives

While coherent narratives about the benefits of interdisciplinary collaboration were common, another frequent but more ambiguous narrative about interdisciplinary collaboration had to do with the challenges involved in practice. Here we add understanding to practices of epistemic culture by analyzing these ambiguities. One challenge of which scientists often spoke was differences in meanings of the same terms and practices. Ambiguous meanings of terms and different interpretations of how best to do even the most basic lab procedures were a source of challenges discussed in describing interdisciplinary collaboration. For example, differences in standard procedures may lead to errors as described by one chemist in the research center: [Differences] could be trivial, like the lab notebook that you write your findings, you find some groups have this way of recording their data, and this lab this other way. Even if it’s reporting data, of course there is a basic scientific format, but you find that some other group, they have a particular way of doing things, and they think that it’s the best. And so there are faults that could be based on, because somebody’s not doing the same thing.

In both academic and industry labs, researchers experienced a number of tensions through collaboration. In academia, tensions often arose when collaboration overlapped with other practices linked to success, such as publishing. One student in the traditional academic lab said, With all of this work, you want some personal gain . . . how much do I let [my collaborator] do, and how much do I keep doing myself for my own thesis. When it comes to collaboration, you always have to ask yourself who gets the credit? Who gets to be the first or second author? There is always that line where you have to

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decide what you’re doing, what you publish. You want to help, and share intellectually, but at the same time you have to be a little selfish and protective.

Issues of credit in collaboration, and particularly interdisciplinary collaboration, were another common source of ambiguity. From our inductive analysis of the data emerged themes that trouble the pat, coherent narratives about the benefits of interdisciplinary collaboration. Not only did the narratives from interviews reveal both the coherence and ambiguity in interdisciplinary collaboration, but the observations also showed the tensions and sources of ambiguous narratives that emerge from working across disciplines. These observed ambiguities within the epistemic cultural practice of interdisciplinary collaboration are described in the next sections. Ambiguity in Academic Practices

The daily practices of scientific work in the labs sometimes created ambiguity for interdisciplinary collaboration, despite the clear and coherent narratives about the value that interdisciplinarity could bring. In the academic settings, the individual competition for personal credit and fame created a tension with interdisciplinary collaboration. Dr.  Michelle, who ran the traditional academic chemistry lab, described the problem of competition in academia in pithy terms. While acknowledging the benefits of interdisciplinary collaboration, Dr.  Michelle could easily recall advice from her former advisor in graduate school about dealing with competitors: “He talked about the need to guard your scientific territory by using stories that involved dogs and hydrants.” The pissing contest metaphor for academia presents some considerable tension with collaboration practices. Perhaps in response to this early advice, Dr.  Michelle’s approach to interdisciplinary collaboration and to dealing with the ambiguity of competition tended to be a division of labor with others outside her area who would normally work in other territory. In the lab, for example, her students would Skype with collaborators in adjacent but separate disciplinary areas (and remote geographical locations) who could provide needed techniques or samples. Because credit is a mechanism in academia that individualizes the efforts of scientists, humor was often employed to express researchers’ ambiguity about personal accountability in an epistemic culture of chemical sciences that purportedly values collaboration and cross-­disciplinary fertilization. In the following excerpt of an observation in Dr.  Paul’s lab (the PI who had industry experience and came back to academia), we can see an example of how credit is really focused at the individual level. At a lab meeting, Martin (a postdoc who is moving out of the lab soon) describes his difficulty with an experiment

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conducted for a collaborative project: “I tried throwing all kinds of things at it. It was a little discouraging because maybe this protein is useless, it’s doing nothing. I hope I can get it to work so I can know that I am not a failure as a scientist.” He laughs slightly after making this last humorous remark. Martin often uses self-­deprecating humor, but as he’s one of the acknowledged leaders in the lab, he knows he’s not a failed scientist. Finally, he says, “I will be done with this experiment soon, but it might be something for a young lab member continue [smiles].” Jeff says in a sarcastic tone, “Hmm, I wonder who that will be.” People laugh, because it is a self-­reference, as Jeff works with Martin and is the young lab member who will take over responsibility for this project. Within an epistemic culture that values interdisciplinary collaboration, a narrow and personalized “ownership” of various projects and the responsibility for their outcomes underlines the importance of individual credit. This tension is mediated through reflexive and self-­deprecating humor. While laughter is used to make light of temporary setbacks in experiments, this humor and joking points out that in the academic settings individuals are indeed responsible—­ and credited—­for the various projects, even if collaborative in nature. Humor signifies ambiguity with interdisciplinary collaboration in academic settings, in revealing power and the structural underpinnings of individual credit. Ambiguity in Industry Practices

In the industry setting we observed, as in the academic settings, the narrative about interdisciplinary collaboration was also resolutely optimistic. In addition, the coherent narrative about the company was that there had been a recent change toward more interdisciplinary collaboration, away from a more “balkanized” disciplinary approach between the separate lab groups that previously existed. Bart, a manager at the firm, told us that the company “was very boxy, segregated, a segregated community of four technical disciplines.” He continued his narrative of transformation from the past to the present by telling us that the company had taken a “right hand turn” (and gestured with his hand) as it introduced a new business model and changed its leadership structure. These changes, he said, led to greater collaboration across the company’s four disciplinary units. This story was told and retold by scientists at all levels in the company. In contrast to the coherent story about new interdisciplinary collaboration, actual practice of sharing across the group and department lines was difficult. For example, the group we observed would often critique the quality of data other disciplinary groups would produce, or complain about the number of samples needing to be analyzed and the amount of time expected to analyze them. The time pressures were built into the structure of the work, with the deadlines and the weekly meeting report-­backs as frequent reminders of how time was

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pressing and workloads were not getting any smaller. The time needed to communicate meaningfully across disciplinary lines was not available in the industry lab. In fact, time pressures led to unintended consequences that would actually undermine interdisciplinary collaboration. Sometimes lab group members would intentionally undermine the efforts of their collaborators in other disciplinary groups. Often this involved an intentional lack of transparency, secrecy, and withholding information. In one example, another group in the company had been requesting information from the group we observed, who had been stalling to fulfill this request. During a meeting, Lucy, one of the bench scientists, raised her idea for how the group can relieve the constant requests to do their work faster: “You know, when you subpoena information from the government, they delay and delay—­until you finally get five hundred boxes. And what you’re looking for is somewhere in those five hundred boxes. I suggest the same approach [with the other group]; show them every bit of data of our group and have them sift through it.” Lucy’s idea is to give the other group some more work in finding the information they need so that the next request will not come so quickly. This strategy of slow down and information overload for those who were meant to be collaborators across disciplinary lines is a response to the pressures of too much work and too little time. Maintaining the group’s autonomy in the practice of interdisciplinary collaboration is a kind of warranting that our data suggest could be a useful addition to the concept of epistemic culture. Interestingly, the bench scientists’ managers in the company give similar advice about maintaining autonomy. At one meeting, when bench scientists complained about members of other groups being “too inquisitive,” Michael—­ the department manager—­suggested that when employees from outside the group visit the lab that his group members “close their computers until they leave.” In another meeting, Patricia (bench scientist) told the group that a sales representative for the company inquired about a project the group had been working on. She smiled, looked around the room, and assured Michael and the group that she “didn’t give him any information.” Apparently, between the scientists in the lab group and the sales department there existed more of a rift than intergroup collaboration. The industry coherent narrative about greater interdisciplinary collaboration becomes more ambiguous when observing the actual practices of the group. Furthermore, in the industry setting, humor was frequently employed to highlight the differences between one’s own group and the group’s collaborators. In one instance, Donald, a bench scientist, sarcastically mentioned that another group in the company “[had] a communication problem” and failed to send in their samples on time. This was met with affirmative smiles, eye rolling, and

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sighs throughout the group, as others nodded in agreement. Here our data show the role of humor helping to define an epistemic culture. This type of collective judgment, often charged with snide remarks about others’ lack of competence, occurred when collaborating groups or individuals missed deadlines or made mistakes in their tasks. In another instance, after experiencing difficulty with a collaborating group, Ashley (a bench scientist) suggested that it would be useful to know which individuals in the other group “submits [samples], who purifies, and who gets the results.” Donald replied, “this might seem a bit harsh, but I don’t think they care about the results.” The absurdity laden in this statement was met with laughter. He continued in a sarcastic tone, “Well, maybe ‘care’ isn’t the right word [faded off].” Everyone continued laughing. The irony here is predicated on tension between the ideal conception of science as data-­driven and the prospect of scientists who “don’t care” about their own data. In another meeting, Michael related a conversation he had with a female supervisor for another group. She had asked that Michael’s group write a protocol for all stages of a certain testing process. But Michael refused, agreeing to do so only for one stage because the group “won’t have enough time to write protocols for everything.” To garner support for refusing to document their work processes, Michael used an interesting tactic—­he turned to Donald and said, “Imagine that you will have to write a protocol for every step in this process?” Donald quickly understood this as a cue to rally behind Michael’s defiant position and answered, “I’d jump out the window [at which remark everyone bursts into laughter].” In other words, by personalizing a problem via a rhetorical question, Michael was able to build consensus in the face of outsiders’ attempts to modify the group’s work habits. And as happened many times before in this group, humor was employed as the unifying way to negotiate ambiguity with interdisciplinary collaboration within the firm. In contrast, when mistakes were made within the group, humor was used to downplay intragroup errors and ease potentially tense situations. For instance, Ben, who was moving the following Wednesday to another company, made a mistake in completing a task in the lab. He publicly apologized to Patricia in a group meeting. During this apology, Michael interjected, “You know Ben, I’m tired of your mistakes. By Wednesday, you’re outta here.” The lab personnel laughed at the joke before moving on to a new discussion without addressing the mistake further. In an environment where success is measured at the organizational level and everyone’s individual performances are evaluated based on their contributions to the organization, mistakes can be potentially detrimental and are looked down upon. In these two cases, we see how humor is used to effectively manage mistakes in ways that confirm the group’s competence. In this case, through the group’s use of humor, they acknowledged Ben’s mistake and also put it behind them. During collaboration with other groups, however, humor was used to draw attention to

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mistakes or to characteristics seen as inadequate or detrimental. In those cases humor seems to stir up the ambiguity in interdisciplinary collaboration.

Discussion and Conclusion We contribute to the concept of epistemic culture (Knorr Cetina 1999) by applying Griswold’s (1987) insights about simultaneous coherence and ambiguity for legitimate cultural objects. We heard the same coherent narratives about interdisciplinary collaboration across all four settings from traditional academic to industrial firm: interdisciplinary collaboration is beneficial if sometimes challenging. Inevitably, chemical scientists had a harder time in making interdisciplinary collaboration than expected. But the difficulties varied by context. The clearest differences were between the academic and industry labs. In academia tensions arise where incentives favor individual credit over collaboration, while in industry the time pressures that lab groups face lead to the construction of barriers between discipline-­based groups. The use of humor to deal with the ambiguities about interdisciplinary collaboration seems to be a prominent mechanism for maintaining coherence within epistemic culture. But even the use of humor varies by context; in the industrial firm humor was often deployed at the expense of other disciplinary groups within the company, while in the academic labs humor was used more at the individual level (i.e., where the tensions about credit reside). If poking fun at collaborators in industry served to highlight the daily inefficiencies and tensions of group efforts, the more reflexive laughter found in academia was aimed at attenuating these contradictions so as to allow scientists to construct more coherent bridges between the individual and group levels. And where the internal ridicule and inside jokes in academia enabled the continuation of collaboration in the face of individualizing trends, self-­humor in the company ultimately reaffirmed a positive in-­group image and, by proxy, continued to question the competency of collaborators in out-­groups. By focusing on how interdisciplinary collaboration looks through the eyes of our respondents, who inhabit the epistemic culture of the chemical sciences, the different scales of epistemic culture can be seen. At one level, epistemic culture is a kind of disciplinary coherence (e.g., means of warranting knowledge, narratives about practices like interdisciplinary collaboration). But on a different scale, one connected to local organizational contexts and enacted practices, the ambiguities in epistemic culture are visible. We next discuss the implications of our study for articulating these two scales of interdisciplinary collaboration—­ the coherent field level and the ambiguous organizational level. At the scale of the field, the rhetoric about funding provides a kind of coercive isomorphism (Powell and DiMaggio 1991) shaping coherent narratives about

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the benefits of interdisciplinary collaboration. Albert et al. (2008) note that these narratives can be shaped by hegemonic field-­level pressures of powerful epistemic cultures like experimental biomedicine. New institutional analysis of organizational fields shows discrepancies between what organizations say they do and what they actually do—­decoupling is a common finding in this literature (e.g., Meyer and Rowan 1977; Hallett 2010; Smith-­Doerr and Vardi 2015; Turco 2012). In this chapter, however, our data led us to see that the challenges of interdisciplinary collaboration are actually part of that narrative coherence, rather than finding a stark decoupling of talk and practice. At the organizational scale, our comparative ethnography allowed us to see beyond the epistemic culture’s coherence to the varieties of ambiguity in interdisciplinary collaboration. In particular, following this analysis and threads from other studies we can see the need for further analysis of the role of humor (Parker and Hackett 2012; Smith-­Doerr and Vardi 2015), time and resource pressures (Vallas and Kleinman 2008; Smith-­Doerr 2005), and instruments (Centellas et al. 2013) in order to better understand the organizational-­level variation in interdisciplinary collaboration. This study’s findings draw attention to how epistemic culture is manifested within particular organizational contexts. Practices of epistemic culture (like interdisciplinary collaboration) are locally instantiated and ambiguous in practice, even if the arrangements by which knowledge is warranted across laboratories may be fairly standardized through documents like lab notebooks and publications or samples. At the day-­to-­day level of epistemic cultures, ambiguities emerge in what interdisciplinary collaboration means, who benefits from this practice, and how it is resisted. We observe that interdisciplinary barriers are sometimes built with bricks like humor, which reinforce existing structures in ways that are difficult to see or to change. notes

We are grateful for research assistance from Claire Duggan and Angela Stoutenburgh. We acknowledge project funding by the National Science Foundation (1064121/1063944, 1413898), but note that all findings and opinions are the authors’ and do not necessarily reflect the views of the NSF. Earlier versions of the chapter were presented at a 2014 workshop held at Wisconsin Institute for Discovery organized by D. L. Kleinman, and at the Eastern Sociological Society and the American Sociological Association 2015 annual meetings. Authors are grateful for helpful comments from Scott Frickel, Daniel Kleinman, Beth Popp Berman, Mathieu Albert, and Barbara Prainsack. Address correspondence to Laurel Smith-­Doerr, [email protected]. 1   Names of individuals, units, and organizations in this chapter are pseudonyms in order to preserve confidentiality.

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4 ◆ Interdisciplinary Fantasy Social Scientists and Humanities Scholars Working in Faculties of Medicine M at h i e u A l b e r t, E l i s e Pa r a d i s , a n d Ay e l e t K u p e r

The literature on interdisciplinary research practices has thus far largely focused on “temporary” interdisciplinary research teams and centers set up to address a specific mission or deliver a particular outcome (see, e.g., Dewulf et al. 2007; Rhoten 2003; Rafols 2007) and on cross-­disciplinary relationships between the social and natural sciences in applied research projects (Brosius and Russell 2003; Heberlein 1988; MacMynowski 2007; Miller et al. 2008; Sievanen et al. 2011). This literature has provided a rich picture of the challenges and benefits of collaboration among researchers with different academic backgrounds on interdisciplinary research projects. However, it has tended to focus on organizations that had not yet been institutionalized into durable social structures. It has therefore captured only a subset of the various types of infrastructures for interdisciplinary collaboration. Although academics have built distinctions between inter-­, multi-­, and transdisciplinary research (e.g., Rosenfield 1992), interdisciplinarity is the most frequently used term in health research (Paradis and Reeves 2012), and is often used as an umbrella term that includes the other subtypes of collaborative research. Thus, we use interdisciplinary here to denote the broader, more inclusive version of interaction across disciplines. In this chapter, we depart from the predominant literature on interdisciplinary research practices in two important ways. First, we investigate interdisciplinary collaboration within highly structured academic institutions (faculties of medicine), whereas most of the existing literature has focused on emerging or 84



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temporary interdisciplinary teams and research centers (e.g., Jeffrey 2003; Rhoten 2003). Second, we focus on social sciences and humanities (SSH) scholars, whose experience has generally been overlooked by analysts (for exceptions, see Albert and Paradis 2014; Albert et al. 2015; Prainsack et al. 2010). Our specific concern is to investigate the integration of these scholars within a research culture for which they were not prepared by their formal education. Our findings suggest scope conditions as a useful refinement to Lamont and colleagues’ (2006, 2009) concept of cognitive contextualization. Cognitive contextualization refers to the process of suspending one’s methodological preference within an interdisciplinary evaluation context in order to adequately assess research endeavors coming from various other disciplines (Lamont 2009). As we show in this chapter, as faculties of medicine in Canada are dominated by an all-­pervading biomedical worldview, they may not be conducive to such cognitive contextualization. Existing scholarship on interdisciplinarity has not sufficiently emphasized that the meeting of disciplines always occurs within social spaces that are neither neutral nor sheltered from power struggles—­whether between different scientific communities or among external stakeholders. When researchers engage in interdisciplinary efforts—­such as through a collaborative project or as newly appointed faculty members in an interdisciplinary unit—­their engagement necessarily takes place within a social environment that predated their entry and that is already structured by a configuration of power relationships among disciplines, epistemologies, and competing definitions of academic excellence. The reality of interdisciplinarity is therefore often far from the fantasy depicted in the literature and in professional discourse: a disembodied meeting of open minds occurring in a neutral space and enabling the free pursuit of complex problems (see, e.g., National Academy of Sciences 2004; Ravid et al. 2013). When practiced, interdisciplinarity is necessarily embedded in a space that is hierarchically structured by the distribution of power and resources (scientific, economic, political, etc.; see Bourdieu 1988). The space within which interdisciplinarity occurs is thus structured both symbolically and materially by its different stakeholder groups: science policy makers, funders, university administrators, and researchers. The interests of these actors, defined by their scientific visions and positions within the configuration of power (Bourdieu 2004), largely determine which research questions are deemed worth investigating and which are discarded as irrelevant. The priority given to some questions over others results in an implicit (and sometimes unwitting) ranking of disciplines, and establishes or perpetuates the power relations between them. With this in mind, the hope that in an interdisciplinary environment new ideas will triumph based solely on their intrinsic merit appears somewhat naïve.

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Researchers themselves also do not come to interdisciplinarity tabula rasa. Prior to their arrival, they have been socialized into a discipline, or an “academic tribe” (Becher and Trowler 2001), and have made their careers within it. They hold situated views on science that include specific understandings of productivity, epistemology, and methods. Interaction may be fairly easy when scientists come from communities that have affinities between their epistemological assumptions or general view of science, which can lead to proximal collaboration (e.g., biochemistry and chemistry); however, epistemic clashes are likely to be triggered when researchers come from more distant communities and create more distal types of relationships (e.g., biomedical and social sciences) (Bauer 1990; Lau and Pasquini 2004). Recent work on the integration of SSH scholars into the health research field substantiates this critical view of interdisciplinarity (Albert and Paradis 2014; Albert et al. 2015; Béhague et al. 2008; Browner 1999; Hemmings 2005). The main tenets of this critical work are that the power relationship among scientific groups has to be taken into account if we are to understand the organization of scientific work and why some research questions are investigated while others are not (Hess 2007). This chapter builds on these earlier studies, exploring new aspects of the experiences of SSH scholars working in faculties of medicine—­ specifically, their experiences of the clinical research evaluation culture. We decided to explore participants’ accounts of their experiences because their narratives offer a unique perspective on the realities of being a SSH scholar in a faculty of medicine. After being confronted with unfamiliar and ill-­fitting evaluation criteria and standards in their institutions, most SSH scholars adapted their research and aligned themselves with the expectations of their new milieu. The presence of these SSH scholars did not, however, result in a revision of that milieu’s evaluation standards that are embedded in the preexisting medical research culture. As a consequence, SSH scholars find themselves in settings where the customary research practices and metrics of success (e.g., laboratory-­ based organization of research, emphasis on having large numbers of publications in clinical journals, and the need for external research funding in order to be perceived as a legitimate researcher) are often inconsistent with their internalized research culture.

The Discourse of Interdisciplinarity: The Canadian Context In 2000, the Canadian government replaced its Medical Research Council with the Canadian Institutes of Health Research (CIHR) (Government of Canada 2000). The purpose of this change was to promote interdisciplinary research on a wide range of health issues and broaden the understanding of illness beyond



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the merely biological. After a transition period lasting nine years (2000–­2009), the Social Sciences and Humanities Research Council completely stopped funding health-­related research projects, forcing SSH scholars to submit their projects to CIHR, a funding body dominated by biomedicine (Albert and Paradis 2014). Echoing CIHR, Canadian faculties of medicine have also become fervent promoters of interdisciplinarity. Among the seventeen faculties of medicine in Canada, twelve have firmly committed themselves to interdisciplinarity and have included this mandate in their current strategic plans (see Albert and Paradis 2014; Albert et al. 2015). The push for interdisciplinarity is rooted in the premise that the integration of perspectives from various disciplines will generate research that is capable of addressing unsolved issues related to health and illness that could not be addressed effectively by traditional disciplinary approaches (NIH 2007). Another core assumption of calls for interdisciplinarity is that every discipline will be able to contribute equally to the research enterprise and that, therefore, none will exert a monopoly over creative solutions (Armstrong 2006). The subtext of the collaborative ideal is that interdisciplinarity can transform the existing academic social order, both symbolically and materially—­that is, in terms of how legitimate science is defined and practiced, and how resources are distributed. In this vision, the traditional social order, characterized by competition and turf wars among the disciplines, would be replaced by a new order that is more egalitarian and collaborative—­one freed from boundary work and disciplinary hierarchies. In this ideal world, no approach, theory, or method would be favored over others. All researchers, regardless of their discipline of origin, would be willing and able to bracket their own scientific identities and their preferred disciplinary research practices in service of the common good. Epidemiologists with MD degrees would readily work closely with anthropologists and accept a three-­or four-­year gap between the beginning of a collaborative research project and the publication of the associated book. Bourdieusian and Marxist SSH scholars would bracket their worldviews and coauthor articles with rational choice economists. The past and current form of science, dominated by struggles for scientific authority and, concomitantly, over resources, would be eclipsed by collegial cooperation and tolerance of all paradigms. This is the dream of interdisciplinarity, a new standard for scientific excellence emerging out of a plurality of perspectives.

Theoretical Framework Our understanding of the misalignment between the promises of the discourse on interdisciplinarity and the experiences of the SSH scholars we interviewed is informed by the concept of decoupling, as well as by Pierre Bourdieu’s concept

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of doxa (1987), which is defined as a “set of fundamental beliefs which does not even need to be asserted in the form of an explicit, self-­conscious dogma” (Bourdieu 2000, 16). Decoupling comes from neo-­institutional theories, which frame organizations as legitimacy-­seeking entities that respond to both legal and normative external pressures (Bromley and Powell 2012; Ramirez 2006, 2010). The interdisciplinary discourse that dominates current scientific policies gets translated at the university level into policies and structures to promote it, without much regard given to the ability of individuals within those structures to deliver on the desired outcomes. Decoupling or loose coupling can exist when the connection between policies, practices, and outcomes is nonexistent or lacking (Bromley and Powell 2012). In the Canadian context, institutional policies present interdisciplinarity as the optimal way to develop solutions to real-­world problems (Hall et al. 2006). They suggest practices such as collaborative problem solving and multidisciplinary evaluation of research activities, which may or may not match the preexisting practices of universities, and which tend to be organized around disciplinary departments ( Jacobs 2013). Moreover, these policies imply outcomes like innovative and holistic solutions, as well as greater research productivity. The experiences of the SSH scholars we interviewed suggest that in faculties of medicine, the policies inspired by interdisciplinary discourse are loosely coupled with practices and outcomes: they do not align with how SSH scholars do research, and do not appear to lead to higher-­quality knowledge production. Most notably, evaluation practices have not enabled SSH scholars working in faculties of medicine to be full participants in an interdisciplinary order—­that is, in an order that equally values and integrates their particular insights. While the concept of decoupling helps us theorize about the discrepancies between interdisciplinary policies and the practice of interdisciplinarity at the organizational level, it does little to help us understand why this situation may be especially challenging for SSH scholars. To explore why that might be, Bourdieu’s concept of doxa is a helpful starting point. For Bourdieu (1994), the doxa of a field is the reflection of the interests of the group that dominates it, and tends to be accepted as universal by all actors in the field, including those who are subordinated. SSH scholars who enter the medical research field are confronted with a doxa that includes assumptions about many dimensions of research, including definitions of productivity and academic excellence, understandings of appropriate sources and levels of funding, and which research questions and methods are considered legitimate. These assumptions influence and reflect how actors in the field value different types of academic activities (Albert et al. 2009).



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Methods Sampling Procedure

Our study recruited faculty members from eleven faculties of medicine in Canada. To be included in our sample, participants had to hold a doctoral degree from a social science or humanities department or program (e.g., sociology, anthropology, history) and have held a primary academic appointment in a faculty of medicine for at least two years. Faculty members holding a clinical degree (e.g., MD, RN) in addition to a SSH degree were excluded; it was hypothesized that their clinical credentials might increase their legitimacy within medical faculties. Participants’ resumes and publications were reviewed prior to interviews. Interviews were audio-­recorded and lasted between sixty and ninety minutes. Follow-­up interviews were conducted as needed. Table 4.1 summarizes the main characteristics of the sample. table 4.1

Main Characteristics of the Sample

Gender Women Men

20 9

Academic rank Professor

8

Associate professor

11

Assistant professor

10

Years on faculty Minimum

2

Maximum

23

Average

11

Median

10

Disciplines

13

Faculties of medicine (/17)

11

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A total of twenty-­nine participants were selected through purposeful and snowball sampling until data saturation (Strauss and Corbin 1998). To identify faculty members who potentially met our inclusion criteria, we searched the CIHR database of successful grant applicants between 2005 and 2013, and perused the websites of known relevant departments and programs within Canadian faculties of medicine. We supplemented this strategy by asking participants to provide the names of other potential interviewees; this snowball sampling allowed us to find participants working in clinical departments who would not have otherwise been found. Ethics approval was obtained from the University of Toronto Research Ethics Board. To preserve the confidentiality of the identity of the participants, we are not including the full list of disciplines of origin. Data Collection

The interview script addressed several aspects of the definition of legitimate research and the evaluation criteria used in faculties of medicine. Participants at the ranks of associate and full professor were asked to describe the criteria used to assess their productivity for promotion. Assistant professors were asked to describe the criteria used by their department head for their annual reviews. This strategy anchored the conversation in decisive episodes of participants’ academic lives and invited reminiscence beyond those episodes. A second set of questions delved into participants’ perceptions and appreciation of local evaluation criteria. A third set assessed participants’ career satisfaction and asked them to identify advantages and drawbacks of their position in a faculty of medicine. Data were analyzed by thematic content analysis. Gender did not come up spontaneously in conversations with participants, and our analysis of experiences by gender did not suggest important differences.

Findings Encountering the Medical Evaluation Culture

When the SSH scholars in our sample were hired into faculties of medicine, they entered a strictly codified social space in which rules, standards, and expectations were unambiguously articulated. New faculty quickly learned the critical elements they needed to have on their CVs to acquire legitimacy and advance through the academic ranks. They were also keenly aware of the importance of conforming to the evaluation norms of their new environment. Evaluation criteria are the crystallization of a field’s doxa; failure to meet those standards was a major hurdle on the path to greater legitimacy and status within the medical research field. As one participant articulated, two measures are integral to



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success, namely “getting research grants and getting publications in high-­impact clinical journals” (SSH10). Another participant concurred: “At my first-­year evaluation meeting, I had three publications, which for social science is great, but when my Chair looked at my CV, it was like: ‘Well you really need to be getting more publications’” (SSH09). These quotes illustrate the lack of cognitive contextualization in Canadian faculties of medicine when it comes to evaluating productivity. Several participants said they were explicitly asked to keep in mind upon joining their unit that they were no longer in the social sciences or humanities field, but in the medical research field. No attention was paid to the disciplinary standards of the social sciences or humanities in the evaluation process. To be successful in the medical field, SSH scholars felt they had to alter their publication pattern to fit with the field’s doxa: “I’ve been told I need to establish my reputation in the clinical research environment and, to do so I need to publish as much as clinical scientists. They said I had to do that regardless of the fact that I’m not a clinical scientist and that my research work is very different in many ways. That’s what I need to do if I am to be taken seriously by the clinical scientists and achieve legitimacy” (SSH29). Other participants interpreted their clinical colleagues’ discussion of the rules of the medical research game as a mandate that must be followed in order to grow in that field: “As a social scientist coming into medicine you have to play a strategic game, you have to be political because if you’re not producing what they’re expecting, there’s going to be a problem, because their expectations are not going to be met” (SSH20). Most participants experienced publishing expectations in medicine as an obstacle to meeting the standards of their home discipline, thus putting them at odds with their training and internalized views on productivity. Many also felt that the publication standards in their home discipline were more stringent (see Albert et al. 2015): “The assessment process does not match the career trajectory of a social scientist, but rather that of the ideal epidemiologist or lab scientist. It does not accommodate the kind of writing we need or should be doing to master our field” (SSH26). Others described how a successful career trajectory in medicine had been portrayed solely in terms of rate of production, or output per unit of time: “When I first started, I was told that I needed to aim for six peer-­reviewed publications a year. I’ve never forgotten that number. If you’re starting out, that six a year would probably be seen as good productivity. I think by the time you get to full professor, it wouldn’t be seen as enough because you ought to be demonstrating a sense of proliferation; you should be a senior author on six and a primary author on three or four” (SSH12). Reflecting on his own position in his department, one participant summarized a sentiment widely shared by others:

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“We are merely guests in the medical domain. We’ve been invited in. We’re playing in their domain” (SSH20). As guests, SSH scholars appear to be excluded from the process leading to the establishment of evaluation criteria. Very few were offered the opportunity to reflect, negotiate, and modify these criteria to match the regime of knowledge production in SSH. Conversely, most perceived they had to create at least a partial rupture with their academic past in order to gain recognition from their clinical colleagues. For many, encountering the medical research evaluation culture was a bewildering experience: they were entering a social world materially and symbolically structured by a doxa both unknown and disadvantageous to them. All rapidly realized that adaptation was required to navigate their career in this new environment. These challenges faced by SSH scholars in faculties of medicine are clearly very different from those faced by SSH scholars engaging in interdisciplinary collaboration while remaining in SSH departments. For the latter, the interdisciplinary experience remains circumscribed to research activities that they can cease if they so choose. For the SSH scholars included in our study, interdisciplinarity includes immersion within a foreign research culture on which they have no influence and from which they cannot withdraw without serious job-­related repercussions. They participate in the “game” of health research, but neither they nor people with comparable professional backgrounds have input into the rules by which they are supposed to be playing. The Impacts of the Accounting Approach to Evaluation

The quotations we have presented suggest the dominance of an accounting approach to evaluation in medicine. Success seems to be defined in terms of counts, or rates of production, that are narrow and quantifiable. However, almost without exception, participants expressed a strong discomfort with using this approach for evaluating their academic merit, seeing it as lacking validity. The following quotation illustrates a common perspective among participants: “Research assessment is meaningless because all it focuses on is number of publications and how much money your grant brings in. To me that says nothing. There’s nothing valid about research assessment here” (SSH15). These counts are determined by the medical research doxa—­that is, by the productivity standards of clinical scientists and epidemiologists—­and fail to recognize the diversity of knowledge production in medicine and which constitutes our participants’ experience. In some cases, the accounting approach appeared quite extreme, as if the primary function of academics was to add papers as fast as possible to a preexisting stack of knowledge: “I was told to do whatever I need to do to get published. They said: ‘You just need the



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publications and that’s all we care about. We just want to see these citations’” (SSH10). Accounting evaluation models also applied to funding. The message most of our participants got when they joined their faculty of medicine was that more equaled better: “They would accept funding from the Social Sciences and Humanities Research Council, that’s fine. But it has to be sizable. Let’s say a fifty-­thousand-­dollar grant would be okay, but the bigger the better, because the metric is different when you’re against bench scientists, or clinical scientists who are pulling in these multi-­million-­dollar grants” (SSH13). This measuring of academic excellence by counting reveals an imposition of the clinical science knowledge production regime upon SSH scholars. While participants reported not being directly advised against publishing in SSH journals, it was clear to them that some journals and types of analysis became out of reach because of the time demands of such writing. Reaching the targeted number of yearly publications—­up to six in some settings—­implies privileging journals with low word counts (i.e., clinical journals, which usually publish articles in the two-­to three-­thousand-­word range), therefore privileging some types of research questions, types of data collection and analysis, and writing styles, as well as selecting certain academic conversations (i.e., in medicine) over others (i.e., in the social sciences). The following quote illustrates how SSH scholars are in principle free to engage in any kind of scholarly work, but in practice, when confronted with the evaluation grid, they are not: Technically we can do whatever we want. But will it prevent people from getting a superlative merit increase? Yes. Will it prevent them from getting promotion to full professor? Yes. I can still do [work according to sociological quality standards] if I want, but it has consequences in terms of career trajectory. If you care about your career trajectory then you can’t spend a lot of time writing papers, you just have to crank them out. (SSH15)

In order to meet the expectations of their work environment without compromising their integrity as SSH scholars, several participants developed a split publication strategy. One stream of publications was crafted for clinicians and published in clinical journals; another was crafted for SSH scholars and intended for SSH journals. The two following quotes exemplify this publication strategy: When I think about publishing I think both about where I’m expected to publish and about what kind of writing I want to do. I’ll give myself the opportunity to write a social science paper once every couple of years. I wouldn’t, in a year, only write social science papers, I would always make sure I had some of the clinical ones. (SSH12)

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I could publish in my disciplinary journals with an impact factor of 0.7. And most biomedical people would hurt themselves laughing. And I could say, “But no-­no-­no, this is a feather in my cap, I’m very proud to be here,” and they’d say, “This is nonsense.” So I think that’s why in some respects I try to occasionally publish in clinical journals just to say, “Look, I’ve done it, don’t bug me.” But I get my personal satisfaction with my publications in nonclinical journals. (SSH21)

Others, especially those in junior positions, were more vulnerable to the judgment of their clinical colleagues and sought to increase their productivity by building opportunistic alliances with others around them, and by submitting their work to any journal that would publish it, at the expense of their own quality standards: “In order to meet their criteria, I try to put my name on as many papers as possible, whether or not they have any interest to me. I also decided to submit to obscure journals to maximize my chance to be quickly published. It’s working, my papers are accepted, but I can’t say I’m proud of my work. It is certainly not good social science” (SSH29). Like any set of evaluation criteria, those developed by clinical researchers align with their own practices, but not necessarily with those of other research communities, especially the SSH scholars with which, as our data show, they share few affinities (see Spaan 2009 for a similar discussion among biomedical engineers, as well as Opthof 2011 for one among basic cardiovascular scientists). Participants were struck by the disconnect between the medical research criteria used to assess them and their actual research and writing practices. The disconnect was not merely about paper length, but rather about the type of arguments SSH scholars are expected to develop for their disciplinary peers—­a process that requires more time, thorough analysis, and a greater engagement with the theoretical and empirical literatures. The next exchange exemplifies how the accounting approach to evaluation is incompatible with SSH research practices: interviewer :

Do you think it’s possible to have as many papers as clinical scientists have and get a better evaluation? participant : It depends what kind of work you do, but the kind of work I do, which is conceptual work, no. . . . If you’re doing thoughtful pieces that contribute to what I think social science is, you can’t possibly produce that much. (SSH05)

In a research environment where academic excellence is primarily defined by an accounting of outputs, books necessarily lose status and appeal, as they are immensely time-­consuming and consequently lower average productivity. Several participants were “stunned” (SSH29) when they were told that books were not considered a major academic achievement: “Books a­ ren’t valued at all. I was told, don’t even bother publishing a book, it doesn’t count for anything. I got the



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same advice for book chapters: it’s a waste of time” (SSH10). The disqualification of books—­and, to a lesser degree, book chapters—­was perceived by many as a dismissal of what represents one of the highest accomplishments in social science and in the humanities. This dismissal left participants dismayed, feeling like their internalized classification system was suddenly made incongruous and therefore inoperative. The contentious status of books eloquently exemplifies the clash between two conflicting research doxai. These publication dilemmas may result in what David Hess calls “undone science” (Hess 2007; Frickel et al. 2010): decisions that bring SSH scholars away from the production of knowledge that they and their communities deem important for improving the health of populations (see also Shostak 2013). In this way, the dominant evaluation culture in medicine may be said to negatively impact our understanding of various socioeconomic and cultural health-­related issues. For example, it is hard to present a sophisticated and sociologically inspired argument connecting patient care to power asymmetries between the health care professions in fewer than three thousand words. Lab-­Based Research Model as an Impediment to SSH Academic Success

Participants emphasized that the evaluation culture in faculties of medicine was not the only impediment to achieving academic success. Several also saw the dominance of the lab-­based research model as an organizational feature playing against them. Clinical and laboratory researchers typically lead research teams in which each research member—­commonly graduate students and postdocs—­ investigates a specific aspect of a larger research question. Because biomedical scientists rarely, if ever, engage in unfunded research, everyone working in the laboratory—­students, laboratory managers, research assistants, and so on—­gets paid. Participants in our study perceived this work organization model as giving an edge to traditional health researchers, who increase their productivity by sharing the workload. SSH scholars usually do not work this way; their research projects are not developed with a view to being segmented into multiple subprojects, and they consequently do not commonly lead large and lasting research teams: I see physicians who hire research coordinators who gather the data. And then they just kind of work with some data set at the end. They even hire a biostatistician to do the stats for them. So, it’s just interpreting tables and looking at it against existing literature on PubMed. Whereas as an anthropologist, I’m going in working with different communities, and understanding local dynamics, and trying to work with and foster certain kinds of processes so that I can interpret the data. The rules of the game are rigged in their favor. They absolutely are. (SSH26)

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Another key difference between traditional biomedical research and SSH research is the role of graduate students. Graduate students in the SSH usually develop their own research projects and do not necessarily have their supervisors as coauthors on their publications. Conversely, in biomedical research, students usually take on a specific part of their supervisor’s research program or project. Moreover, supervisors are typically coauthors on all project-­related publications, leading to a multiplication of publications they can list on their CVs. Participants in our study saw the difference between these two ways of working as detrimental to them: “The faculty of medicine works on that science model where there’s a lab and you hire doctoral students or postdocs who plug in like a piece of the puzzle into your research lab. That’s completely counterintuitive to the social science model where students come up with their own projects and work on them with supervision” (SSH14). In some faculties of medicine, SSH scholars are faced with another hurdle. Regardless of their home discipline, every faculty member has to provide financial support to the graduate students he or she wants to supervise. This rule works well for bench and clinical researchers, who usually have access to larger amounts of funding than SSH scholars do (see Albert and Paradis 2014), but it is a barrier to graduate supervision for SSH scholars: “You can have a student with a 95 percent average who doesn’t have a fellowship, and if you don’t have a grant to take them on full-­time, you can’t do it. Of all of the issues of being a social scientist working in the faculty of medicine, this is the biggie, for us. We talk about it all the time” (SSH14). Disconnect between Assessors and Assessed

SSH scholars also find themselves in the difficult position of experiencing a disconnect between assessors and assessed. As many participants indicated, they are evaluated by researchers who, they perceive, lack the expertise to soundly evaluate their work, and who are not their chosen nor primary audience. Put differently, the disciplinary community with whom SSH scholars wish to engage barely overlaps with the community that evaluates their work. The following exchange exemplifies this challenging situation: participant :

People in my [department] assess me, but they’re not my community. I have to meet their criteria, but they are not my true peers. interviewer : Do you mean that the people you want to interact with are not the people who are assessing you? participant : Yes. It’s a difficult position. I suppose that once a year I do my assessment with them and I fight all my battles at that moment, and then I ignore them for another year and they ignore me for another year. (SSH01)



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Unsurprisingly, the key reason SSH scholars do not see their clinical colleagues as being able to assess their work is that they do not have the appropriate knowledge to do so. More specifically, according to several participants, their colleagues have neither an interest in nor an understanding of social theory. Taken together, this lack of knowledge and lack of interest lead to a misinterpretation of SSH, and to a false and unproductive attempt to separate theoretical and applied SSH research: They don’t understand what the social sciences have to offer. . . . What social scientists bring in medicine is social science theory; it’s the discipline, the ideas, the thinking; that’s what social scientists have that is so precious. But that isn’t shared. Social science theory, like . . . “This isn’t about theory, this is about applied work. We don’t need theory for applied work.” They don’t understand the role of a theory in generating questions. (SSH05)

The need to correct traditional health researchers’ misperceptions of SSH was a recurrent theme throughout the interviews. Most participants pointed out that they have to educate their colleagues about the types of questions SSH scholars ask, the methodologies they use, the role of theorization, their writing style, and their productivity. Since SSH scholars’ career advancement within faculties of medicine depends on the support and approval they get from their clinical colleagues, educating them becomes critical. The following quote exemplifies how SSH scholars deploy such educational efforts: I’ve been on review committees where everyone else is from a lab and they don’t understand social science. I once had to defend a proposal that was a superb piece of qualitative work, and I had to defend it like it was my own. For forty minutes I couldn’t let it go because it was so good, and I couldn’t let them give it a bad score. Their issue was that the results would not be generalizable. And finally someone on the committee said, “You know what? What are we doing? Here is the expert, and the expert is saying this is a great piece of work.” I could have kissed her. And so the thing was funded, but it was a lot of hard work. (SSH06)

Because of the power imbalance between SSH scholars and clinical researchers within faculties of medicine, the commonly used methods in clinical research and epidemiology—­which are largely quantitative—­rarely face real opposition. Therefore, clinical research practices are perceived within the field as the universal gold standard (Goldenberg 2006; Timmermans and Berg 2003), and anything departing from this standard becomes in need of justification (see Béhague et al. 2008 for a discussion of power differentials between anthropology and epidemiology). SSH scholars are constantly placed in the position of having

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to explain, demand, fight for, and advocate for their own work and that of their peers. Some participants doing predominantly qualitative research have grown frustrated by constantly having to justify what they do: “I am tired of justifying to number-­crunchers why qualitative research is valuable. I’m just sick of that conversation. I don’t feel that I should constantly be in the position of having to educate people who expect me to know their stuff and they don’t respect what I do” (SSH08). Other participants reported having sincerely tried to change things, but with no success: “There are a few of us within the department who try [to make some changes], but we’ve also developed this kind of helplessness, where despite our best efforts, we’re talking to the wind” (SSH09). It is important to note that traditional health researchers’ misunderstanding of social science and humanities research does not necessarily result from an intentional or conscious prejudice against SSH scholars. Instead, this misunderstanding can be seen as a consequence of the doxa of the medical research field—­that is, its axiomatic assumptions about research, productivity, and work organization, and how these assumptions get materialized in structural arrangements, including an evaluation culture that legitimizes and facilitates some research practices while delegitimizing and discouraging others (Albert and Paradis 2014). While SSH scholars have been invited to join faculties of medicine under the auspices of interdisciplinarity, their participation has not led to an evolution or transformation of the clinical research doxa. Positive Experience with Assessment

Although a majority of participants mentioned having issues with the dominant evaluation culture in their faculty, a minority described a different reality, saying that criteria incompatible with their research practices had not been imposed on them. Rather, their different way of thinking was recognized and encouraged in their unit: “I’ve received no direction on where to publish at all. My chair is really happy to have somebody saying different things and trying to push different ways of thinking” (SSH22). Another participant had a similar experience. Academic pluralism was celebrated in his department, and accordingly, evaluation criteria were applied with flexibility: “The number one thing I would say is that my department has been forward-­thinking in recognizing that academics can contribute in different ways and you can’t have too regimented a system for evaluating academic performance” (SSH24). Similarly, while most SSH scholars in our study reported they had developed two streams of production to satisfy the different requirements of their unit and their own disciplinary standards, a minority of participants said they did not have to live a divided academic life: “I’ve not felt I had to mould to anything. I



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might have had to explain what I’m doing. I do critical work, and I publish in different places than clinical journals. But that’s accepted” (SSH17). Some participants explained that they were able to maintain their research practices primarily because their work environment was welcoming to SSH research. However, as much as they may have benefited from a receptive work environment, further description of their realities revealed some ongoing challenges. One reported that he had been able to sustain his social science practice because SSH scholars in his department form a cohesive group and coordinate efforts to ensure that they are all fairly assessed. SSH scholars have attained a critical mass, and were thus able to garner sufficient leverage to make themselves heard: “There’s always been a tension about how to make things fair for social scientists in the department. But we’re pretty active, we’re vocal; we make sure that there is room for doing things the way that social scientists do things” (SSH14).

Discussion and Conclusion Our findings complement the recent body of work on interdisciplinary peer-­ review evaluation (Huutoniemi 2012; Lamont 2009; Lamont et al. 2006; Mallard et al. 2009), which has shown how panelists on interdisciplinary funding panels perform “cognitive contextualization” (Lamont 2009; Mallard et al. 2009) to reach decisions they consider fair. Cognitive contextualization occurs when panelists suspend their own methodological preferences and evaluate proposals “according to the epistemological and methodological standards that prevail in the discipline of the applicant” (Lamont 2009, 132). It presumes that panelists acknowledge that different research methods are equally valuable, and implies that panelists are willing to defer to the expertise of colleagues from other disciplines when the proposal under review is outside the scope of their own knowledge. Fair evaluation is thus defined as the application of appropriate disciplinary criteria and a respect of disciplinary sovereignty (Lamont 2009; Mallard et al. 2009). In contrast with what Lamont and colleagues observed on interdisciplinary peer review committees, our findings show that cognitive contextualization is largely foreign to the evaluation culture in Canadian faculties of medicine—­at least with respect to the SSH. Similarly, seeking evaluative fairness is not customary. How can we reconcile our findings with those of Lamont and colleagues? One key difference between the deliberative processes of interdisciplinary committees and the faculties of medicine where our participants work is that every committee member is equal when making an evaluative judgment: one individual equals one vote. Consequently, there is a relative power balance among the various perspectives and disciplines represented. In contrast, faculties of medicine tend to be epistemologically and methodologically homogeneous,

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such that experimental research remains the gold standard against which other scientific approaches are evaluated (Holmes et al. 2006; Albert and Paradis 2014; Albert et al. 2009). Moreover, the power structure between the disciplines is well established, and SSH scholars are outnumbered by biomedical scientists in faculties of medicine (Albert and Paradis 2014; Albert et al. 2015). SSH scholars do not hold the same number of votes as clinical scientists on any committee—­ related to, for example, hiring, promotion, governance—­and their research is therefore not equally valued. Clinicians are symbolically and structurally in a position of authority over nonclinicians, and this authority gives their representations of research a legitimacy, cemented into the medical research doxa, that members of the interdisciplinary committees examined by Lamont do not have. What is perceived to be fair, or adequate, is not the outcome of cognitive contextualization, but rather a judgment anchored in the dominant evaluation culture. A similar situation would likely occur in any interdisciplinary setting where one scientific group exerts control over symbolic and material resources (see MacMynowski 2007 and Strang 2009 for similar power differentials between the social sciences and the natural sciences in environmental research). Another important factor to consider in comparing our results with those of Lamont and colleagues is the fact that all panelists in their study were SSH scholars. Lamont and colleagues thus studied proximal interdisciplinarity. In contrast, the interdisciplinarity characteristic of Canadian faculties of medicine can be called distal. In proximal interdisciplinarity, differences in epistemic habitus are present and observable, but limited, while in distal interdisciplinarity they reach deeper, and may be less commensurable. Therefore, as our findings suggest, specific conditions need to be met for cognitive contextualization to occur. In sum, the results of our study show that the promise of inclusiveness at the heart of interdisciplinarity has yet to materialize into fair evaluation criteria for SSH scholars: criteria that are anchored in the disciplinary standards of scholars. We argue that this challenging situation results, at least in part, from the decoupling between interdisciplinary research policies and the enduring doxa of biomedicine, whose inertia hinders the integration of SSH scholars into the field of medicine (Albert and Paradis 2014). In order for SSH scholars to fully participate in the knowledge production enterprise of the health research field, faculties of medicine need to develop a better awareness of the fact that multiple or hybrid systems are necessary for fair evaluation of research emerging from various disciplines (Huutoniemi 2012; Lamont 2009; Mallard et al. 2009). Cognitive contextualization may be the most helpful strategy for encouraging epistemic diversity in medical research. Leaving it to SSH scholars to alter their practices to meet the medical doxa contradicts the principles behind interdisciplinarity: that experts from different disciplines will collaboratively use their differing knowledge and training to create innovative solutions to enduring problems.



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note

This research was supported by the Social Sciences and Humanities Research Council of Canada, grant 410-­2011-­0639. The authors wish to thank Scott Frickel and Barbara Prainsack for their insightful feedback on several versions of this chapter. references

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Opthof, Tobias. 2011. “Differences in Citation Frequency of Clinical and Basic Science Papers in Cardiovascular Research.” Medical & Biological Engineering & Computing 49 (6): 613–­621. Paradis, Elise, and Scott Reeves. 2012. “Key Trends in Interprofessional Research: A Macrosociological Analysis from 1970 to 2010.” Journal of Interprofessional Care 27 (2): 113–­122. Prainsack, Barbara, Mette N. Svendsen, Lene Koch, and Kathryn Ehrich. 2010. “How Do We Collaborate? Social Science Researchers’ Experience of Multidisciplinarity in Biomedical Settings.” BioSocieties 5 (2): 278–­286. Rafols, Ismael. 2007. “Strategies for Knowledge Acquisition in Bionanotechnology: Why Are Interdisciplinary Practices Less Widespread Than Expected?” Innovation 20 (4): 395–­412. Ramirez, Francisco O. 2006. “The Rationalization of the University.” In Transnational Governance: Institutional Dynamics of Regulation, edited by M. L. Djelic and K. Sahlin-­Anderson, 225–­244. Cambridge: Cambridge University Press. ———. 2010. “Accounting for Excellence: Transforming Universities into Organizational Actors.” In Higher Education, Policy, and the Global Competition Phenomenon, edited by V. Rust, L. Portnoi, and S. Bagely, 43–­58. London: Palgrave. Ravid, Katya, Russell Faux, Barbara Corkey, and David Coleman. 2013. “Building Interdisciplinary Biomedical Research Using Novel Collaboratives.” Academic Medicine 88 (2): 179–­184. Rhoten, Diana. 2003. A Multi-­method Analysis of the Social and Technical Conditions for Interdisciplinarity Collaboration. San Francisco: Hybrid Vigor Institute. http://www.ncar.ucar .edu/Director/survey/Rhoten_NSF-­BCS.FINAL.pdf. Rosenfield, Patricia L. 1992. “The Potential of Transdisciplinary Research for Sustaining and Extending Linkages between the Health and Social Sciences.” Social Science & Medicine 35 (11): 1343–­1357. Shostak, Sara. 2013. Exposed Science: Genes, the Environment, and the Politics of Population Health. Berkeley: University of California Press. Sievanen, Leila, Lisa M. Campbell, and Heather M. Leslie. 2011. “Challenges to Interdisciplinary Research in Ecosystem-­Based Management.” Conservation Biology 26 (2): 315–­323. Spaan, Jos A. E. 2009. “Biomedical Engineering and Bibliometric Indices for Scientific Quality.” Medical & Biological Engineering & Computing 47 (12): 1219–­1220. Strang, Veronica. 2009. “Integrating the Social and the Natural Sciences in Environmental Research: A Discussion Paper.” Environment and Sustainable Development 11 (1): 1–­18. Strauss, Anselm, and Juliet M. Corbin. 1998. Basics of Qualitative Research: Techniques and Procedures for Developing Grounded Theory. Thousand Oaks, CA: Sage. Timmermans, Stefan, and Marc Berg. 2003. The Gold Standard: The Challenge of Evidence-­ Based Medicine and Standardization in Health Care. Philadelphia: Temple University Press.

5 ◆ Some Dark Sides of Interdisciplinarit y The Case of Behavior Genetics A a r o n Pa n o f s k y

Interdisciplinary research has long been a hot topic. It seems that whenever a university administrator or a grant maker wants to signal an effort to kick research into overdrive, to innovate with new rapidity or in new domains, or to solve great social challenges, the label “interdisciplinary” is called to do much of the work (Brint 2005; Jacobs and Frickel 2009). Scholars have noted that the idea of interdisciplinarity has accompanied the growing disciplinary organization of the university since early in the twentieth century. Now and again, however, an overheated rhetoric of interdisciplinarity is invoked in response to perceived crises of the limits of disciplines (Abbott 2001; Moore 2011). Scholarly discussion of interdisciplinary science often takes its benefits mostly for granted and considers instead how best to overcome barriers to setting it up (e.g., Committee on Facilitating Interdisciplinary Research 2004). Academic writing on interdisciplinary science usually ascribes to it several epistemic advantages over research conducted under the auspices of disciplinary fields. Disciplines, it is argued, are intellectual silos that are too specialized and self-­contained to tackle many important problems. Because they are self-­ justifying, they produce repetitive and abstract knowledge that rigidly adheres to their own pet concepts, methods, and styles of thought. As a result they are both incapable of understanding their own epistemic limits and seeing beyond their own horizons to direct scientists’ attentions to problems of practical, worldly importance. Interdisciplinary science, in contrast, is all about assembling scientists of diverse expertise to bridge the intellectual gaps between these silos. In making these connections, interdisciplinary science overcomes the limits of hyperspecialization allowing forms of knowledge production driven by novelty, 107

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adapted to overcoming epistemic limits, and devoted to responsible solutions to practical problems (Frodeman 2014; Klein 1996; Taylor 2010; see also Jacobs and Frickel 2009; Moore 2011). Jacobs (2014) has recently shown empirically that disciplines actually do not suffer from the epistemic deficits of which they are often accused. Furthermore, interdisciplinary fields usually need to adopt disciplinary structures to remain viable over the long term. The present chapter shares Jacobs’s skepticism about the overheated rhetoric in favor of interdisciplinary science through a case study of one interdisciplinary field, behavior genetics (see also Moore 2011). My argument overall is that behavior genetics is an interdisciplinary field that suffers from many of the epistemic shortcomings supposedly endemic to disciplines—­ communication is fragmented, knowledge production is repetitive, certain epistemic assumptions are held rigidly rather than debated openly. I show also that as a consequence of interdisciplinary organization, field members have difficulty policing research that they see as scientifically irresponsible. I have a secondary argument, similar to one Albert, Paradis, and Kuper make in this volume, that “interdisciplinarity” be seen not as an abstract principle of certain scientific spaces, but as an active stake in struggle among scientists and that the competition among scientists having competing visions of what interdisciplinarity is can affect the social structures shaping knowledge production. Behavior genetics is the field, often in the media, claiming genetic influence on behavioral traits like intelligence, personality, mental illnesses, criminality, and even television watching and political opinion. Behavior geneticists come from many different disciplines including psychology, psychiatry, neuroscience, genetics, and the social sciences, though I will show that this mix has changed substantially over time. And researchers study humans as well as nonhuman animals. The field has historically been highly controversial, especially around issues of race and intelligence; debates have been animated at the highest level by the question of whether and how much genes constrain aspirations to improve society and reduce inequality. I have written elsewhere about how behavior genetics has coped with these controversies and how they shaped its social and scientific development (Panofsky 2014). I approach behavior genetics as a field in the sense proposed by Bourdieu (2004). A field is twofold: at once a “field of forces” or rules of action that shape what actors do and a “field of struggles” among actors both for status within a given arrangement of forces and over the shape of the forces or social structures themselves. “Scientific capital,” or the symbolic and material resources to make claims in a given scientific space, is the ostensible target of these struggles. Put differently, scientific fields are spaces where scientists struggle to accumulate scientific capital and to define scientific capital, which is to say the rules of what counts as good science in a given domain. In Bourdieu’s ideal type of

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the autonomous scientific field, what differentiates science from all other fields is that the producers and consumers of scientific capital are the same people. Appeals to other authorities are, at least in principle, illegitimate, and thus each bid for scientific recognition must be made with respect to one’s fiercest competitors. This intense reflexivity and competition is what, according to Bourdieu, gives science its particularly dynamic and productive character. As I deploy “interdisciplinarity” in this scheme it refers both to the structural forces and in part to the struggles organizing the field. As Turner (2000) argues, structurally speaking, disciplinary fields are closed labor markets for producing and hiring graduate trainees. The basic structural condition for an enduring interdisciplinary field like behavior genetics is that it draws members from many other disciplines but lacks the integrating effect that an internal labor market produces. In terms of the struggles organizing behavior genetics, key among them are conflicts among scientists trying to impose different visions of interdisciplinarity that privilege different kinds of research and different relationships among members of the field and relations to other fields. The field approach opens the door to a naturalistic and empirical approach to interdisciplinarity. Rather than offering a particular, substantive definition of interdisciplinary science, I trace the different ways that behavior geneticists have constructed interdisciplinarity as both a set of forces organizing the field and the stake of struggles. In this sociohistorical, simultaneously structural and cultural approach, we will see that scientists’ conflicts about behavior genetics changed its interdisciplinary character over time, and also that there are multiple, competing visions at any given moment. The analysis presented in this chapter draws on thirty-­six in-­depth interviews with scientists from different traditions within behavior genetics as well as critics of the field (see Panofsky 2014). By asking informants to situate themselves vis-­à-­ vis other researchers, I reconstructed the main opposed positions and visions in the field. These interview data were supplemented with the published record of scientific articles and commentaries. Together I used them to reconstruct the field’s history, major controversial turning points, and, for this chapter, struggles surrounding interdisciplinarity. The first part of the chapter traces the role of interdisciplinarity in behavior genetics’ origins. I show that interdisciplinarity was both a scientific and a political strategy to distance the field from its distasteful historical legacy and grow the field. I also show how the field’s interdisciplinary structure was reorganized and fragmented less by scientific problems than through efforts to cope with a public controversy about IQ and race. The second section looks at how in the aftermath of that episode, different groups of behavior geneticists developed different understandings of its interdisciplinary character that were connected to their different practices for accumulating scientific capital. Psychologists’

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preferred organization of interdisciplinarity came to dominate, and it privileged understandings and practices that emphasized behavior genetics as a set of tools and questions rather than a robust network of scientists interested in mutual competition and recognition. In the third section I consider the epistemic consequences of the way interdisciplinarity came to be organized and practiced in behavior genetics. In contrast to the idea that interdisciplinary fields have distinct epistemic advantages, I show that behavior genetics’ interdisciplinary character sustains epistemic structures that limit and constrain knowledge production. The chapter concludes by highlighting some of the lessons about interdisciplinarity from the behavior genetics case.

The Origins and Fragmentation of Behavior Genetics Behavior genetics’ history is often narrated with a focus on the conceptual innovations that made possible arguments about the relative effects of inherited and acquired factors leading to human differences. Accounts trace the links among Francis Galton’s 1875 invention of the twin method, statistical refinements of the method by Fisher and Pearson, the application of these methods to questions of human character by the early twentieth-­century eugenics movement, and the discrediting of the genetic investigation of behavior for its association with the racism and eugenics of Nazism during World War II (Kevles 1985). The field’s modern disciplinary history thus begins with a new cohort of scholars who in the 1950s and 1960s sought to revive the genetic analysis of behavior as a legitimate topic and knew that they had to actively break with the topic’s distasteful history. How did behavior geneticists seek to do this? First, they sought to limit their ties to the older generation of scholars who had played active roles in the promotion of eugenic ideas as the point of genetic analyses of behavior. They enrolled Theodosius Dobzhansky, the eminent population geneticist and great public critic of racist interpretations of genetics, as an advocate. Second, they actively pushed eugenics and ideas of racial hierarchy off the field’s agenda. They emphasized both their political distastefulness and their scientific limitations (Fuller and Thompson 1960, 1, 327). For example, they elevated the radical genetic uniqueness of each individual as an attack on the coherence of racial comparisons (Hirsch 1968), and they showed how the old eugenic anxiety about the superfecundity of the feebleminded was a myth (Higgins et al. 1962). Though behavior geneticists did not use the term, they believed that several features of interdisciplinary organization would be instrumental in ensuring the field’s scientific bona fides and distancing it from these politicized topics. One aspect of this was to draw in scholars from a very wide range across the behavioral and life sciences—­psychology, genetics, developmental biology, psychiatry,

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anthropology, sociology, demography, agricultural research, behavioral ecology, and ethology, among others. An explicit aim was to combine the intellectual concerns of these different disciplines in order to broaden the intellectual possibilities of behavior genetics; to enrich understandings of concepts like culture, population, and development; and, crucially, to keep nature versus nurture analyses from taking over the agenda. Interdisciplinarity was seen as instrumental to both the field’s scientific goals as well as its intellectual legitimacy and collective stability. Many field members also believed that bringing human and animal researchers into a closer relationship could also establish behavior genetics on firmer scientific footing. Animal researchers had taken much of the lead in assembling the behavior genetics network, and they saw the juxtaposition of animal and human research as opening up the study of general laws of behavior. Though some were worried about “hopelessly befuddled” (Bliss 1962, ix) human research, most behavior geneticists understood that the field’s potential was linked to its capacity to illuminate human behavioral issues. The hope was that animal research, with its “pure science” orientation, objective definitions of behavior, and focus on physiological mechanisms, might be a model for human researchers to follow. Behavior geneticists thus sought to create a new interdiscipline mixing the expertise and interests of researchers from many different disciplines and perhaps to create a new transdiscipline in which a new form of science would emerge distinct from the mixture of disciplinary backgrounds making it up. Behavior genetics’ interdisciplinarity was seen as the means both to enrich the domain intellectually but also to establish it on truly scientific grounds separate from the unsavory political entanglements that had consumed some of its forebears. Furthermore, these aspirations aimed to help behavior genetics capitalize on the postwar expansion of the university and proliferation of new interdisciplinary fields (such as neuroscience, cybernetics, and medical genetics). As such, the field’s founders busied themselves with holding conferences and training workshops and publishing symposia and textbooks. These efforts culminated with the founding in 1970 of the field’s society, the Behavior Genetics Association, and its journal, Behavior Genetics. What is crucial to note, therefore, is that interdisciplinarity was more than an intellectual or scientific enterprise, or merely a way to get people together to do better research. It was a political strategy for marginalizing harmful ideas and aligning the field with organizational trends in the university system. This optimistic period and in particular the interdisciplinary solution to behavior genetics’ controversial potential was soon disrupted. In 1969 Arthur Jensen wrote a lengthy article in the Harvard Educational Review titled “How Much Can We Boost IQ and Scholastic Achievement?”. Jensen drew from a diverse array of animal and human behavior genetic studies to claim that

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intelligence is largely genetic in origin, that the IQ gap between blacks and whites was due to genetic differences not discrimination. He used behavior genetics to argue American social structure was largely a product of genes and that policy efforts to reduce inequality through compensatory education were doomed to fail. Behavior geneticists suddenly found themselves in the middle of precisely the kind of controversy they had hoped to avoid. Critics attacked both Jensen and behavior genetics vehemently. Dozens of critics, but most prominently geneticist Richard Lewontin and psychologist Leon Kamin, attacked behavior genetics’ empirical, methodological, and conceptual basis and charged the field with a politically conservative bias (e.g., Kamin 1974; Lewontin 1970). Facing extreme pressure, behavior geneticists felt it necessary to defend Jensen along with their field, and thus became indelibly, if uncomfortably, linked with him.1 The reactions to this controversy throughout the 1970s profoundly restructured behavior genetics. Scholars from across the social and biological sciences severed their ties to behavior genetics. The disciplinary profile of the field shifted toward psychology, and in particular psychometricians and clinical psychologists interested in macro-­level human behavior. Animal behavior geneticists formerly at the field’s center found themselves marginalized. Behavior genetics was still interdisciplinary, but in a different way. It became fragmented into an “archipelago” of approaches. Rather than a transdiscipline where relations between inheritance and behavior would be explored in a variety of ways, the “behavior genetics” label became applied to one version of that relationship: a small set of islands where the main task was the partitioning of trait variance for some population into genetic and environmental components (and where higher genetic estimates were preferred). Islands where inheritance/ behavior links were explored in more evolutionary, developmental, ethological, sociocomparative, or ecological ways became alternative and opposed to behavior genetics. Finally, the behavior genetics islands of the archipelago (psychology, psychiatry, and animal behavior genetics) were overtaken by a kind of bunker mentality in the wake of the race and IQ controversy of the 1970s. Criticism of the field had been so vehement that behavior geneticists became fiercely protective of the field and distrustful of the motives of anyone who would raise sharp questions about its research, even field members. The field still featured routine practices of peer review, but, as I will illustrate, it became a process where deeper criticisms of behavior genetics assumptions or findings were discouraged. Bourdieu (2004) describes the scientific field as distinguished by the necessity of scientists to seek recognition from peers who have the greatest incentive to withhold that recognition. In his ideal type, this inwardly focused structure leads to an intense competition among scientists and also mutual criticism because

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of the incentive to withhold recognition from those whose work does not withstand scrutiny. As I will demonstrate, in behavior genetics, that competition for recognition became less intense. I will trace a conflict among behavior geneticists to impose different interdisciplinary visions of the field, and the vision that prevailed was one where mutual competition and concern were dissipated. In this dominant vision membership in the field required no deep commitment to mutual competition and much recognition could be taken for granted.

Competing Visions of Interdisciplinarity Prior to 1969, behavior geneticists took it for granted that the field was an interdiscipline, which combined scientists interested in the topic from different disciplinary backgrounds, that aspired to be a transdiscipline, in which a new form of research expertise irreducible to the contributions of neighboring disciplines might emerge. But in the two decades after the race and IQ controversy of the 1970s, behavior genetics’ interdisciplinary character became a stake in the struggle among different kinds of behavior geneticists seeking to define and control the field. There were three basic visions of interdisciplinarity articulated most commonly by three different types of behavior geneticists: animal behavior geneticists, psychiatric geneticists and molecular geneticists, and psychologists. Animal behavior geneticists lamented the field’s fragmented state and yearned for an organization of the field that would be more integrated socially and intellectually. One described behavior genetics (using the term “field” in the commonsense rather than the Bourdieusian way) as so fractured so as to no longer be a collective research domain in any meaningful sense: [Behavior genetics] is not a field. It’s a bunch of different groups of people who are trying to study different questions, many of whom have completely insulated themselves against the outside world and scientists in other fields. They actually don’t want to know what other people say about them, because they’re so sure of the value of what they’re doing. . . . It seems to me to be completely antithetical to how you’d want to do good objective science. (Interviewee 2)

This speaker and many other animal behavior geneticists I interviewed described behavior genetics as divided, mutually distrustful, and defensive. They believed that the field’s history of controversy (beginning with the race and IQ controversy but manifesting in other disputes) was linked to the community’s disorganization and disinterest in mutual engagement. Some described failed efforts to cross-­fertilize among varieties of behavior genetics. This was also a self-­serving vision because, like the field’s founders, they imagined that a more

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integrated research collective would find their rigor as biologically oriented animal researchers more respected and to be a model for quality science. A second set of researchers, psychiatric and molecular geneticists, also viewed the field as fragmented and cited this as a reason to keep their own personal investments in the field low. One speaker told me, “A big problem here is trying to map out a separate domain of behavior genetics, because there is no such separate domain. I mean there’re a few people who are geneticists and behaviorists and who don’t do other things, but they don’t really make breakthroughs, you know” (Interviewee 13). For him, to be labeled a “behavior geneticist” was to be stuck on a backwater island without scientific relevance. Rather it is better to be a molecular biologist who studies behavior and uses a variety of methodologies to understand the “origins” of behavior in terms of “functional gene variation” and the “pathway of that gene” to the behavior in question. Another psychiatric geneticist described the behavior genetics domain as a set of “social clubs” with different “union cards.” His approach was to avoid getting “too caught up in the assumptions and the traditions of any one club” (Interviewee 6). The perception of these researchers and the animal behavior geneticists is not so very different. Behavior genetics is a fragmented domain where different islands have strong and distinct views and are basically uninterested in working outside their own perspectives. For many animal researchers, this is a problem that should be solved by socially and intellectually integrating the multidisciplinary space. For many psychiatric researchers, this is a problem to be avoided by following intellectual questions but limiting one’s personal investment in the social dynamics of the space. The third set of researchers, psychologists using genetics, was most comfortable with the “behavior geneticist” label. They tended not to see the field as fragmented. Or, more precisely, they sat on the dominant island in the archipelago and, taking this position for granted, tended not to think about the other competing islands as salient to their own position. For these field members, behavior genetics is not really a field at all but a part of psychology, and high-­level complex behavior psychology not neurobiological psychology. One field leader described behavior genetics as a “support group, especially during those hard times” during heated controversies. But the rest of the time behavior genetics is best seen as “a tool not a school.” He said, “I don’t want it to be a specialty” and its scientific aims can be best met by “giving the field away” and by working with other behavioral scientists and showing them “you don’t have to be a geneticist to do this stuff ” (Interviewee 28). These psychologist behavior geneticists were committed to behavior genetics mainly as a set of tools to engage intellectual struggles and forge partnerships in other fields (particularly sectors of psychology). “Behavior genetics” was largely a means toward other ends, and cultivating the field as an end in itself (e.g., by

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moving the different intellectual islands in the archipelago more closely through professional or conceptual links) was uninteresting to them. They were content to reproduce “behavior genetics” as a subfield of psychology and were largely indifferent to animal researchers’ alternate vision or the task of drawing the psychiatric researchers closer. As I argued elsewhere, these competing visions and uses of the field’s interdisciplinary structure are linked to the different value and exchangeability of behavior genetics’ “scientific capital” (Panofsky 2011). Scientific capital is the material and symbolic resources necessary for researchers to make credible claims in a particular domain. But scientific capital is field specific, so some sort of conversion or exchange process must take place to give it value in a different context. Behavior genetics capital has been most valuable in psychology where researchers have used it to provoke prevailing theories and empirical approaches. The value has been not in settling controversies but in keeping them running, spurring debates, and keeping cycles of research going. For psychiatrically oriented researchers, behavior genetics has been less provocative, because the idea that mental illness is partly genetic is taken for granted, and less interesting in a field devoted to clinical results and pharmacology. Thus, behavior genetics has been of some interest but ultimately not a strong source of scientific capital in those fields. For animal researchers, behavior genetics has been a barrier for connecting and exchanging with other fields. On the one hand it is seen as politicized, and on the other it is seen as weak on the ecological/social interactions and neurodevelopmental mechanisms that interest most animal behaviorists. Animal researchers I interviewed often fretted about the lack of recognition they received from other researchers and their neighbors’ refusal to see their own work as “behavior genetics” despite substantive connections. Why do animal behavior geneticists remain? Well, many of them don’t, and those who do often lament those lost connections. Those who do remain do so, first, for reasons of tradition, past investment, and disposition. They have long been invested in the field and find that inertia easy to sustain, and furthermore some have a commitment to their alternate vision of the field, though they currently lack the influence to execute it. Second, neither the psychological behavior geneticists’ dominance nor the animal researchers’ weakness was complete. For example, animal researchers are still elected to the leadership of the Behavior Genetics Association, publish in its journal, and are competitive for its prizes (Panofsky 2014). Animal researchers I interviewed expressed discontent with the recognition their science receives, but received enough to maintain their involvement. Thus, psychological behavior geneticists saw the field as a means to create capital in their home discipline; its controversies and risks increased its value. Psychiatric researchers were ambivalent about the field because it was risky and

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not highly valuable in their home discipline. And animal researchers wished behavior genetics was more robust and intellectually integrated like a discipline because they were more or less stuck within it. The upshot for the present argument is that behavior genetics’ interdisciplinary organization has taken a centrifugal structure. Most of the field members are oriented outward—­their participation in behavior genetics is more of a means toward achieving their ends in other fields. In contrast with Bourdieu’s ideal type where scientists must struggle constantly to secure the recognition to maintain membership, field participants could take each other’s recognition more or less for granted and not invest their efforts heavily in building behavior genetics as a community. Behavior genetics hosts competing visions of its interdisciplinary character and potential, but these reflect the field’s weak center of gravity and low commitment of its members. Behavior genetics is structured by a decidedly interdisciplinary set of rules—­those who would see it in quasi-­disciplinary terms have lost—­so what are some of the intellectual implications of this structure and the ethos that sustains it?

Repetitive and Provocative Knowledge Production There is a popular notion that interdisciplinary fields have an epistemic advantage over traditional disciplines ( Jacobs and Frickel 2009; Klein 1996; Moore 2011): unfettered by disciplinary strictures, interdisciplinary scholars can break from repetitive concerns to produce truly novel ideas, make intellectual connections across disparate domains, and free themselves from discipline-­based blinkers to perceive epistemic limits clearly and how to overcome them. But in each of these ways behavior genetics exhibits the supposed problems of disciplinary knowledge production much more than the alleged protean capacities of interdisciplinarity. First, behavior genetics has been highly productive, but the style of productivity has been highly repetitive. The core method of behavior genetics has been to estimate the heritability of behavioral traits in particular populations. This is done by gathering sets of twins or adoptees and blood and adoptive relatives, comparing their scores on tests of intellectual ability, personality, mental illness, and so on, and calculating the proportion of the variance in those scores that can be accounted for by genetic relationships and environmental exposures. However, as early as the 1950s behavior geneticists had declared that “how much” a trait was genetic or environmental was no longer scientifically interesting, hoping to redirect the field’s ambitions to questions of “how” genes and environment influence each other (Anastasi 1958). But after the Jensen controversy in the 1970s and up to the present, estimating heritability became the central task. One psychological behavior geneticist told me that for much of the field’s

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history, “you could get the you-­name-­it questionnaire, give it to a couple of hundred twins, compute the heritability and publish it” (Interviewee 35). This pattern has been reproduced with the advent of molecular genetic technologies since the completion of the Human Genome Project. An important research technique has been to search a set of candidate genes to see if particular variants are more common in people who exhibit a particular trait relative to controls. Political scientists Evan Charney and William English (2012) surveyed over six hundred articles linking 159 traits to variants in four genes (MAOA, 5-­HTT, DRD2, and DRD4). Traits ranged from well-­trodden ground like intelligence and novelty-­seeking personality to “confirmation bias susceptibility” and “creative dance performance.” Charney and English note that there are nearly as many disconfirmations as confirmations and suggest that none of the positive claims for genetic association are credible. The issue at stake for the present argument is that both the heritability studies and the newer molecular studies fit a characteristic pattern. Behavior geneticists have availed themselves of a set of portable methods that can be applied to any number of topics and populations. At the same time the general form of the research is highly repetitive. Thus if critics of disciplinary science accuse disciplines of producing repetitive knowledge that lacks relevance to other intellectual domains, then something similar can be said about behavior genetics. Its publication record is highly repetitive—­what vary most are the trait under study and the population sampled. But within each of the two main research methods (quantitative genetics and molecular association studies), there is relatively little variation and the basic insight is very often similar: this trait too is genetic and environment has less influence than we thought. Second, interdisciplinary science is often touted as enabling scientists’ ability to make novel intellectual connections. How does this go in behavior genetics? On the one hand, behavior geneticists have clearly enabled connections with the psychologist members’ ethos of “giving the field away” and the idea that “you don’t have to be a geneticist” to do behavior genetics research. They have been absolutely instrumental in bringing “genetically informed” designs (which exploit twins and families to disentangle genetic and environmental confounding) as well as molecular genetic techniques to psychology and the social sciences. But from another perspective these connections have been limited. To the extent that behavior genetics is an intellectual trading zone or crossroads, the menu of goods exchanged has been limited, and as I just explained the connections have produced a style of research that emphasizes the extensive application of the same techniques to more topics rather than the intensive investigation of a particular topic using a variety of methods. However, these forms of cross-­disciplinary connection have occurred within a broader context of intellectual fracture and disjuncture. As I argued

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earlier, behavior genetics’ founders envisioned it as a “big tent” where many different disciplinary traditions, epistemological projects, and research agendas might come together, illuminate each other, and hopefully produce a new emergent science. But this vision of a field devoted to the genetic inheritance of behavior in all its varieties failed. The label “behavior genetics” became associated with a particular set of disciplines and genetic techniques. In particular “psychologists who estimate heritability” became the dominant identity. Furthermore, behavior genetics was not the field where genetic heritability was theorized and debated from all angles, but rather the field where the concept was defended, where it was given strong often quasi-­causal interpretations, and where high estimates of heritability were implicitly preferred. Other ways of linking inheritance and behavior—­such as evolutionary psychology, ethology, developmental systems theory, behavioral ecology, neurogenetics—­ were seen as different communities disconnected and opposed to behavior genetics rather than alternative scientific perspectives among a wide range of scholars engaging each other in a competition for a common form of scientific capital. The complaints of animal behavior geneticists and ambivalence of psychiatric geneticists suggest the seeming impossibility of forging connections to the field other than those driven by psychological behavior geneticists (Interviewees 3, 7, and 11). Thus, behavior genetics’ interdisciplinary structure has encouraged some kinds of intellectual connections in parts of the psychological and social sciences, but it has also led to disconnection and intellectual isolation of researchers concerned with related questions across the biological sciences. Research on the relationship between heredity and behavior is done by many different separated sets of researchers with little connection to the field labeled “behavior genetics.” Advocates of interdisciplinarity charge disciplines with getting fixed in their assumptions and lacking the resources for self-­critical reflexivity. Has behavior genetics been a source of greater epistemic reflexivity and a place where epistemological blind spots of the disciplines have been exposed? Perhaps the behavior genetics’ greatest claim to fame is its attack on the (supposed) “blank slate” orthodoxy in psychology and the social sciences (Pinker 2002). Behavior geneticists have long measured their impact as making the claim that “genes matter,” that traditional social science research often confounds genetic and environmental causes, and, more controversially, that genes limit many social ends (e.g., reducing social inequality) we might hope realistically to achieve. Furthermore, since behavior genetics has so often been a focus of intellectual controversy (not least because its members have willfully provoked alleged “blank slate” traditions), its methods and claims have been subject to uncommon intellectual scrutiny. These controversies have forced behavior geneticists to defend their research in unusual detail.

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However, behavior genetics’ epistemic reflexivity has had its limits. We can see this in the way behavior geneticists interpreted the heritability concept relative to some of their intellectual competitors. Behavior geneticists often described genetic heritability in quasi-­deterministic terms: high figures suggest “if environmental interventions are to succeed, they must be truly novel ones, representing kinds of treatments that will be new to most populations” (Rowe 1994, 223).2 They reached this conclusion by showing high heritability—­a population-­and environment-­specific statistic—­in many different contexts and by arguing that the environmental influences they discovered were mostly random effects, not the conscious socialization choices of parents and institutions (Plomin and Daniels 1987). As a result they reasoned that people’s genes are making them “choose” their environments and that, barring situations of abuse, most individuals have parents and schools that are “good enough” for them to reach their genetic potential (Scarr 1992). But non–­behavior geneticists used similar data to show a much greater role for environmental effects. They noted that behavior geneticists underestimate environmental effects because they rarely measure them directly, rather seeking to “control” environmental influence using twins and adoptees as subjects. Stoolmiller (1999) showed that adoptions usually occur into a higher socioeconomic stratum. This produces a range restriction that deflates the apparent influence of family on IQ variance. Dickens and Flynn (2001) showed that high heritability of IQ could be consistent with environmental influences causing the substantial secular increases in IQ known as the “Flynn effect.” In their model very small environmental differences can, through feedback loops, magnify into big performance differences. Rather than genes determining environmental choices, as behavior geneticists claimed, genes were part of a complex and dynamic system of practice and reinforcement. The point for my argument is not that either side is right or wrong, but that theories of interdisciplinarity predict greater epistemic reflexivity, yet behavior geneticists were locked into one way of viewing the problem while alternatives came from outside. What do these examples tell us about the epistemic possibilities of this interdisciplinary space? A common claim is that disciplines remain locked in a particular set of epistemic assumptions, but interdisciplines allow novel scientific connections and the ability to gain a more flexible, multivalent view that sees beyond the assumptions. The behavior genetics case, however, seems to demonstrate the opposite. Behavior geneticists did not have a wide range of interpretations of genetic heritability, rather they interpreted these estimates as demonstrating the limited influence of environment. Those who showed how the same data interpreted differently could illustrate a prominent role for environment in intellectual differences came from other disciplines, not behavior genetics. It was not behavior geneticists who demonstrated reflexivity about the

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epistemic limits and possibilities of their models (e.g., the range restriction of adoption studies or the dynamic interpretation of gene/environment correlations), it was these non–­behavior geneticists who did so. Behavior geneticists have tended to favor a particular set of interpretations of their genetic models in opposition to the views of those from other fields. Behavior genetics, in contrast to what advocates of interdisciplinary science have argued, found itself locked into a set of epistemic assumptions. Indeed some of the behavior geneticists I interviewed said they felt that forms of epistemic critique aiming to break out of habitual patterns of thought were perceived by their peers as disloyal. One animal behavior geneticist described his failed efforts to publish critiques of articles in Behavior Genetics: they “were saying something like, ‘this is an honest effort to analyze behavior and basically you shouldn’t criticize that.’  .  .  . You stand by each other, and you don’t hang your dirty laundry outside for people to see” (Interviewee 7). A psychological behavior geneticist explained his view that the field has not developed a tradition of sympathetic critique: “To this day I think people associate any kind of theoretical philosophical work with getting slammed and getting called a racist. There’s no tradition of relatively sympathetic theoretical treatments” (Interviewee 35). The muddled norms about critical theorization of behavior genetics ideas are part of the legacy of the IQ and race controversy of the 1970s. Distrust and defensiveness spawned by this legacy have chilled critical discourse within behavior genetics. It is important to note that behavior geneticists’ provocative interpretations of their research (as in the heritability examples just discussed) were not simply due to the epistemic constraints of the interdisciplinary field’s structure. They were also part of the practices for valorizing the field’s scientific capital that were linked directly to the field’s interdisciplinary structure—­the view of it as a “tool not a school,” which privileged external recognition from other scientific communities (Panofsky 2011). Interpreting their science in ways provocative to dominant framings in “socialization science” was not merely a scientific commitment but an important means for gaining visibility and recognition for many behavior geneticists. Claims about “good enough” schooling and parenting were the culmination of years of a strategy for recognition through attacking intellectual commitments of neighboring fields. As one psychological behavior geneticist described the approach, “They’re not going to listen unless you hit them with a two-­by-­four” (Interviewee 28). Provocation, with the epistemic concomitants discussed earlier, was a productive strategy for behavior geneticists, but it did cause them difficulties. Behavior geneticists were often viewed with suspicion: they faced “real opposition in psychology: ‘Who are these maniacs?’” as one psychological behavior geneticist described (Interviewee 35). Provocation raised the field’s profile but

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often through negative attention. Even worse was the way provocation associated behavior genetics, since the early 1970s, with racial researchers. Arthur Jensen was the start of this for the field, but there were many others who found an uneasy sanctuary in behavior genetics—­Hans Eysenck, J. Philippe Rushton, Richard Lynn, Lynda Gottfredson, Glayde Whitney, Charles Murray, and Richard Herrnstein were the most prominent. Because these scholars were associated with the field, behavior geneticists whose work had nothing to do with race sometimes found themselves viewed as racist or sympathetic with racism (Interviewee 11). Behavior geneticists generally did not see race research as a legitimate part of their field. They typically interpreted it to be of low quality empirically. One psychological behavior geneticist said a colleague “was livid at Rushton for one of his books . . . he just thought the analysis was completely flawed” (Interviewee 23). And furthermore “the methodology of quantitative behavioral genetics is appropriate for analyzing individual differences within groups, but it’s just not appropriate for analyzing individual differences between [racial or other] groups,” as one behavior geneticist explained (Interviewee 8). Even though behavior geneticists thought racial research did not meet the quality or conceptual standards of their field, they found themselves tolerating and even defending racial researchers. As one psychological behavior geneticist stated, they “would never be taken as a spokesman by the rest of us. . . . But I can’t spend my time and energy . . . saying, ‘Get these guys out of it; they don’t represent us.’ One of the prices that I’m willing to pay for academic freedom is to have . . . outliers” (Interviewee 14). This tolerance of, and at times support for, racial researchers followed from behavior geneticists’ strong commitments to scientific freedom that was rooted in the calls for the abolition of their field during and after the IQ and race controversy. It was also linked to the muddling of norms for legitimate scientific criticism among field members given the opposition they faced. But more generally, it was a consequence of the departure from the robust vision of the field as an inwardly oriented transdiscipline favored by many of the field’s founders. When behavior genetics became a weak, poorly policed field and members more interested in building capital in other fields rather than solidifying scientific foundations within, it became a fertile environment for exploitation by racial provocateurs. The particular interdisciplinary structure of the field initiated a set of social dynamics that had particular intellectual and epistemic consequences: knowledge production was repetitive, it became locked into a set of assumptions, and intellectual provocation leading to polarized controversy became an important source of scientific capital. None of these intellectual outcomes is among the supposedly intellectual salutary qualities of interdisciplinary science according

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to its champions, and, indeed, behavior genetics’ intellectual features—­which are directly connected to its interdisciplinary structure—­are much closer to the supposed pathologies of disciplinary knowledge production.

Conclusion Behavior genetics may have a unique history and structure, but I think it offers substantial lessons for understanding the dynamics of scientific fields and, in particular, for problematizing the ways we conceive of interdisciplinarity and imagine its possibilities for improving knowledge production. First, people have long argued that there are many different forms “interdisciplinarity” can take. The framework I have presented opens the door to thinking about interdisciplinary fields having a history where they transition from one form to another. I have emphasized scientists’ complex motivations beyond simple intellectual gain for pursuing versions of interdisciplinary organization. These included political legitimacy and policing of delegitimizing topics (in the field’s early years) as well as different practical strategies for pursuing scientific capital and recognition (among the different types of behavior geneticists in its later years). But beyond demonstrating these points about interdisciplinary fields having complex histories and motivations, my analysis of behavior genetics shows that interdisciplinarity is more than a description of science’s organization. Rather, it is a stake of struggle among scientists battling to establish the field’s boundaries and rules of action. Different visions of interdisciplinarity benefit different kinds of scientists doing different kinds of science. This analysis of behavior genetics suggests a path for analyzing “interdisciplinarity” in a less essentialist and abstract way by linking it to concrete struggles for authority among scientists. Though my analysis is very different from theirs, these points echo the arguments in the chapters by Light and adams and by Albert, Paradis, and Kuper in this volume. It is safe to say that the main excitement about interdisciplinary science is driven by the idea that it has a set of epistemic advantages over traditional disciplinary fields. These include capacities to make novel intellectual connections, to break out of disciplinary ruts and diversify intellectual products, to combine perspectives and thus bring new ways of thinking to problems. I have characterized some of behavior genetics’ features as a domain of knowledge production with respect to these ideas about interdisciplinarity. To summarize, behavior genetics has, first, been characterized by a repetitive style of research where a specific toolkit has been applied widely to different behaviors and populations. Second, the domain of interest in the inheritance of behavior is highly fragmented; intellectual communities do not much exchange with each other. Third, the field’s

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epistemic reflexivity seems limited, and finally, provocation is an intellectual way of life to such a degree that some ideas that cross the line epistemically even by the field’s own standards (such as racial comparisons) have become extremely difficult to police. There are two lessons to draw from this. First, we need to move away from an essentialist understanding of interdisciplinarity to seeing it in terms of social processes that must be studied empirically. From a science policy point of view it would be a mistake to assume that interdisciplinarity per se will deliver better knowledge production than disciplines. The idea here that interdisciplinary fields need not automatically lead to salutary epistemic outcomes is one echoed by Albert, Paradis, and Kuper in this volume and elsewhere (Albert and Paradis 2014; Albert et al. 2015). Their studies emphasize divisions imposed by power imbalances and differences in disciplinary habitus, while mine emphasizes a contingent historical development through controversy that produced a set of “rules” governing the field that led to fragmentation and deintensified scientific competition. But both lines of research suggest the importance and complexity of power in the contingent arrangements of interdisciplinary knowledge production. Second, what is it about interdisciplinarity in behavior genetics that leads to these epistemic problems? Bourdieu (2004) described the ideal-­typical specificity of the scientific field as a space where the legitimate producers and consumers (of knowledge) are the same people. This makes the competition for recognition/scientific capital especially intense. What has happened in behavior genetics is (1) that a history of controversy has fragmented the field so that all the different kinds of scientists, who from a substantive point of view should be competing for each other’s recognition, do not pay much attention to each other and (2) that those who dominate behavior genetics are outward looking and seek recognition in other fields while being able to take recognition within the field for granted. Both trends, fragmentation and centrifugal organization, have deintensified the struggle for scientific capital within the field and have had the various knowledge production implications discussed in this chapter. What binds behavior geneticists together is little more than substantive interest and a shared history of conflict with outside fields. Thinking about Frickel’s (2004) well-­known study of genetic toxicology will make these points a bit clearer. Interdisciplines, he argues, have “intentionally porous organizational, epistemological, and political boundaries” (Frickel 2004, 269). Genetic toxicology mixes scientists from diverse disciplinary backgrounds, from academic as well as government, industry, and NGO organizational settings, and its researchers mix their scientific and their normative (health and environmental safety) motivations in their research choices and judgments. In this way it is more heterogeneous than behavior genetics, but Frickel shows that

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it is strongly integrated. Controversies fragmented behavior genetics, but they galvanized genetic toxicology into a movement. Frickel links genetic toxicology’s innovativeness to its boundary-­blurring structure, but his account also shows that its scientists share a robust investment in the mutual competition to secure each other’s recognition. The lesson of behavior genetics is that without the latter condition, interdisciplinary destructuring is epistemically dangerous. So the question becomes, how can interdisciplinary fields avoid tendencies to fragmentation and centrifugal organization? Most of the literature on interdisciplinary science focuses on short-­term and topic-­based gatherings of scientists. Perhaps the deficits of structure I have highlighted are less apparent in shorter time spans, but I would argue that some kinds of structures are necessary to direct interdisciplinary researchers seeking recognition toward each other if interdisciplinary research enterprises are to be more sustained. Interdisciplinary fields or even long-­term interdisciplinary projects will have structural difficulties sustaining a robust internal competition for scientific capital insofar as they draw researchers from other disciplinary fields, each of which hosts its own distinct scientific capital struggle. Disciplines are organized to embed these capital struggles around a closed labor market (Turner 2000), and Jacobs (2014) argues that insofar as they are successful over the long term, interdisciplines tend to organize themselves along quasi-­disciplinary lines. I am not arguing that interdisciplines must seek the disciplinary solution over the long term, but I am suggesting that they flirt with the epistemological dark sides of interdisciplinary science discussed in this chapter if they do not confront the problems posed by fragmentation and centrifugal organization over the long run. notes 1   See Panofsky (2014, chap. 2) for more detail. 2   Heritability is considered to be not a measure of genetic causation but a descriptive sta-

tistic about the sources of trait variance in a given population in a given environment. See Kaplan (2000) and Lewontin (1974).

references

Abbott, Andrew. 2001. Chaos of Disciplines. Chicago: University of Chicago Press. Albert, Mathieu, and Elise Paradis. 2014. “Social Scientists in the Health Research Field: A Clash of Epistemic Habitus.” In Handbook of Science, Technology, and Society, edited by Daniel Lee Kleinman and Kelly Moore, 369–­387. London: Routledge. Albert, Mathieu, Elise Paradis, and Ayelet Kuper. 2015. “Interdisciplinary Promises Versus Practices in Medicine: The Decoupled Experiences of Social Sciences and Humanities Scholars.” Social Science & Medicine 126: 17–­25. Anastasi, Anne. 1958. “Heredity, Environment, and the Question ‘How?’” Psychological Review 65 (4): 197–­208.

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Bliss, Eugene L. 1962. Roots of Behavior. New York: Harper. Bouchard, Thomas J., and Matthew McGue. 1990. “Genetic and Rearing Environmental Influences on Adult Personality: An Analysis of Adopted Twins Reared Apart.” Journal of Personality 58 (1): 263–­292. Bourdieu, Pierre. 2004. Science of Science and Reflexivity. Translated by R. Nice. Chicago: University of Chicago Press. Brint, Steven. 2005. “Creating the Future: ‘New Directions’ in American Research Universities.” Minerva 43 (1): 23–­50. doi:10.1007/s11024-­004-­6620-­4. Charney, Evan, and William English. 2012. “Candidate Genes and Political Behavior.” American Political Science Review 106 (1): 1–­34. Committee on Facilitating Interdisciplinary Research. 2004. Facilitating Interdisciplinary Research. Washington, DC: National Academies Press. Dickens, William T., and James R. Flynn. 2001. “Heritability Estimates versus Large Environmental Effects: The IQ Paradox Resolved.” Psychological Review 108 (2): 346–­369. Frickel, Scott. 2004. “Building an Interdiscipline: Collective Action Framing and the Rise of Genetic Toxicology.” Social Problems 51 (2): 269–­287. Frodeman, Robert. 2014. Sustainable Knowledge: A Theory of Interdisciplinarity. New York: Palgrave Macmillan. Fuller, John L., and William R. Thompson. 1960. Behavior Genetics. New York: John Wiley. Higgins, James V., Elizabeth W. Reed, and Sheldon C. Reed. 1962. “Intelligence and Family Size: A Paradox Resolved.” Eugenics Quarterly 9 (2): 84–­90. Hirsch, Jerry. 1968. “Behavior-­Genetic Analysis and the Study of Man.” In Science and the Concept of Race, edited by M. Mead, T. Dobzhansky, E. Tobach, and R. E. Light, 37–­48. New York: Columbia University Press. Jacobs, Jerry A. 2014. In Defense of Disciplines: Interdisciplinarity and Specialization in the Research University. Chicago: University of Chicago Press. Jacobs, Jerry A., and Scott Frickel. 2009. “Interdisciplinarity: A Critical Assessment.” Annual Review of Sociology 35 (1): 43–­65. Jensen, Arthur R. 1969. “How Much Can We Boost IQ and Scholastic Achievement?” Harvard Educational Review 39 (1): 1–­123. Kamin, Leon J. 1974. The Science and Politics of I.Q. Potomac, MD: Lawrence Erlbaum. Kaplan, Jonathan M. 2000. The Limits and Lies of Human Genetic Research: Dangers for Social Policy. New York: Routledge. Kevles, Daniel J. 1985. In the Name of Eugenics: Genetics and the Uses of Human Heredity. New York: Knopf. Klein, Julie Thompson. 1996. Crossing Boundaries: Knowledge, Disciplinarities, and Interdisciplinarities. Charlottesville: University of Virginia Press. Lewontin, Richard C. 1970. “Race and Intelligence.” Bulletin of the Atomic Scientists 26: 2–­8. ———. 1974. “The Analysis of Variance and the Analysis of Causes.” American Journal of Human Genetics 26: 400–­411. Moore, Rob. 2011. “Making the Break: Disciplines and Interdisciplinarity.” In Disciplinarity: Functional Linguistic and Sociological Perspectives, edited by F. Christie and K. Maton, 87–­ 105. London: Bloomsbury. Panofsky, Aaron L. 2011. “Field Analysis and Interdisciplinary Science: Scientific Capital Exchange in Behavior Genetics.” Minerva 49: 295–­316. ———. 2014. Misbehaving Science: Controversy and the Development of Behavior Genetics. Chicago: University of Chicago Press. Pinker, Steven. 2002. The Blank Slate. New York: Viking.

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Plomin, Robert, and Danielle Daniels. 1987. “Why Are Children from the Same Family So Different from One Another?” Behavioral and Brain Sciences 10: 1–­16. Polderman, Tinca J. C., Beben Benyamin, Christiaan A. de Leeuw, Patrick F. Sullivan, Arjen van Bochoven, Peter M. Visscher, and Danielle Posthuma. 2015. “Meta-­analysis of the Heritability of Human Traits Based on Fifty Years of Twin Studies.” Nature Genetics 47 (7): 702–­709. Rowe, David C. 1994. The Limits of Family Influence: Genes, Experience, and Behavior. New York: Guilford. ———. 1997. “A Place at the Policy Table? Behavior Genetics and Estimates of Family Environmental Effects on IQ.” Intelligence 24 (1): 133–­158. Scarr, Sandra. 1992. “Developmental Theories for the 1990s: Development and Individual Differences.” Child Development 63: 1–­19. Stoolmiller, Mike. 1999. “Implications of the Restricted Range of Family Environments for Estimates of Heritability and Nonshared Environment in Behavior-­Genetic Adoption Studies.” Psychological Bulletin 125 (4): 392–­409. Taylor, Mark. 2010. Crisis on Campus: A Bold Plan for Reforming Our Colleges and Universities. New York: Knopf. Tucker, William H. 1994. The Science and Politics of Racial Research. Urbana: University of Illinois Press. Turner, Stephen. 2000. “Disciplinarity and Its Other.” In Practising Interdisciplinarity, edited by P. Weingart and N. Stehr, 46–­65. Toronto: University of Toronto Press.

6 ◆ A Dynamic, Multidimensional Approach to Knowledge Produc tion R ya n Li g h t a n d jimi a d a m s

Interdisciplinarity implies a resistance to the traditional boundaries structuring academic research. This unboundedness is inherently temporal: after all, if an interdisciplinary project sticks around long enough, it runs the risk of evolving into what it seeks to resist (i.e., a traditional discipline). Contemporary debates on the best social organization for addressing pressing scientific and/ or social problems provide a valuable step away from the nearly universal push toward interdisciplinarity ( Jacobs and Frickel 2009; Jacobs 2013). While funding continues to pour toward interdisciplinary organizations and strategies within universities, critical approaches to interdisciplinarity may help clarify exactly when a problem demands solutions generated outside of disciplinary boundaries. To proponents the promise of interdisciplinarity rests on efforts to transcend disciplinary boundaries to address complicated problems (Campbell 1969; Klein 1990): disciplines are overly specialized and cannot respond to the multidimensional problems of our day. To opponents the danger of interdisciplinarity consists of a massive reshuffling of resources with limited benefit: disciplines provide an effective social organization for the production of knowledge, especially given the specialization required for addressing difficult contemporary problems. Navigating these opposing positions requires a critical lens capable of tracing the production of knowledge through its various forms. Recent research places greater emphasis on the temporal dimension of interdisciplinarity by recognizing that the organization of knowledge is a dynamic process ( Jacobs 2013; Leydesdorff and Schank 2008). Moving beyond static 127

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discussion of whether interdisciplinarity or disciplinarity is the best organization for a particular problem, critical approaches to interdisciplinarity have argued that the worst case scenario described earlier is likely: interdisciplinary projects turn into disciplinary projects as power brokers construct and manage boundaries to their benefit. This process mirrors the social closure common in status hierarchies as groups battle over limited resources. As seemingly benign interdisciplinary projects gain greater attention, they seek greater rewards, such as financial compensation for faculty and more graduate student labor. Interdisciplinary projects build an organization—­often resembling a discipline—­to garner and protect these rewards. While this tendency toward organization and resource hoarding may be true, the dynamic process is hidden by an over-­determined trajectory. We propose softening this determinism to consider more seriously the boundary work that takes place during knowledge production. In this chapter, we build a dynamic model of academic research that focuses on how research changes based on the characteristics of the boundaries that separate academic fields. Interdisciplinarity, disciplinarity, multidisciplinarity, and transdisciplinarity become particular potential states in the dynamic life course of a research field.1 These different states hold implications for practitioners whose work is bounded by the characteristics of a particular state, at a particular time. In addition we emphasize that boundary work is a dynamic process: an analysis of the boundaries of knowledge production requires a focus on how boundaries are challenged and how they change. For example, a research field, like American studies or biochemistry, may shift from an interdisciplinary project to a more disciplinary one, and interdisciplinary research may grow less visible as a result ( Jacobs 2013; Kohler 1982). The shape of the trajectory that a knowledge project assumes at a given moment in time is important for evaluating its status as a many-­disciplined project, or the broad class of approaches, including multidisciplinarity and interdisciplinarity, that contrast with disciplinary ones. Considering the life course of a knowledge project is key to the development of a critical lens for analyzing knowledge production. Knowledge production takes place multidimensionally. Individuals compete for resources within organizations or groups; organizations or groups are embedded in fields. While these dimensions overlap and reinforce one another, they are analytically separable. In sum, we offer a multidimensional life course model of knowledge production. We begin by developing a theoretical model that hinges on the role of boundary work in structuring knowledge production. We specify boundary work as a dynamic process that shapes the life course of a knowledge project. Next, we describe how this life course perspective operates across the multiple levels of analysis from the individual project to the field. To illustrate this theoretical model, we tease apart several empirical trajectories spanning both projects and fields, including research on religion, demography,



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and HIV/AIDS. Conclusions illustrate the importance of developing robust critical approaches to interdisciplinarity grounded in comprehensive theoretical and methodological tools.

Developing the Model: Many-­Disciplined Boundary Work Prior work has advanced a typology of knowledge production that consists of four categories. These categories—­disciplinarity, multidisciplinarity, interdisciplinarity, and transdisciplinarity—­are often presented as static characteristics of knowledge projects. A static view likely overemphasizes the stability of many-­ disciplined approaches to knowledge production. The danger of this overly static approach is that highly organized projects may be able to cash in on the language of many-­disciplined projects without actually engaging in any cross-­pollination between disciplines. To reorient this static perspective, research has turned to the boundary metaphor ( Jacobs 2013). Boundaries within knowledge production exist along multiple dimensions. The first wave of research on boundaries identifies the lines drawn between science and the public (Gieryn 1999). The second wave of research on boundaries observes the boundaries drawn between groups within science itself ( Jacobs 2013; Lamont and Molnár 2002). One of the key characteristics of the boundary framework is the acknowledgment that the lines drawn between groups—­either within science or between science and the public—­are produced and maintained with a good deal effort, or boundary work. While boundary work takes multiple forms, its primary dimensions involve expulsion and expansion (Gieryn 1999). Expulsion occurs within knowledge production as actors, such as scientists, defend their turf against rivals and, at times, concede questions that are beyond reach or seem less worthy of defense. Expansion, on the other hand, consists of efforts to colonize a new research area—­either one that is already the focus of a rival group or an otherwise unexplored territory. For example, physicists engage in expansion when turning their attention to macro-­social-­scientific problems, such as the study of large-­scale social networks (see Newman 2008), while social scientists engage in expulsion when they try to resist this potentially co-­optive move by physicists by pointing out redundancies or reinventions by the new network specialists (see Freeman 2004, 164–­167).2 Beyond offering a compelling narrative structure for internecine academic wars, boundary work provides a key mechanism for evaluating many-­disciplined projects as new problems are subject to strategic engagement by interested actors. This final logic motivates proponents of interdisciplinarity who see contemporary social and scientific problems as—­at their core—­ requiring the attention of multiple approaches spanning several disciplines (Taylor 2010). Rewards are extended to projects that meet these criteria. Yet, as

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Jacobs (2013) points out, boundary work does not stop here, but continues past the distribution of initial rewards as expulsion and expansion efforts continue.

Toward a Dynamic Multidimensional Model of Knowledge Production Our theoretical model takes this dynamic boundary work as its starting point and assumes that knowledge production consists of multiple strategies for engaging boundary crossing. We focus on two particular dimensions: group-­and individual-­ level motivations for crossing boundaries. Group motivations recognize that formally organized groups, such as preexisting disciplines, will develop strategies focused on expansion and expulsion within fields. These motivations might be resource based (i.e., struggling for financial rewards, lab space, graduate students) or idea based (i.e., struggling to be a leader addressing a particular issue). Alternatively, individual motivations recognize that boundary work occurs at a more micro scale as individual people and projects perform boundary tasks. Within an individual project, such as a research paper, scientists may engage multiple disciplines or may bound their arguments within a single discipline.

Interdisciplinarity

Transdisciplinarity

Disciplinarity

Multidisciplinarity

Actor Boundary-Crossing Low Figure 6.1.

A conceptual model of academic boundary work.

Project Boundary-Crossing High

Project Boundary-Crossing Low

Actor Boundary-Crossing High



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Figure 6.1 summarizes our theoretical approach. The model distinguishes the actor or individual dimension on the y-axis and the project or group dimension on the x-axis. Both of these dimensions vary by level of boundary crossing indicating whether these types of boundary work are priorities or not. These dimensions provide a comprehensive framework for considering the aforementioned types of organization—­disciplinarity, multidisciplinarity, interdisciplinarity, and transdisciplinarity. Disciplines, for example, focus on boundary maintenance and, therefore, are generally less directed toward boundary crossing at the group or individual level. Consistent with Jacobs and Frickel’s (2009) definition, however, multidisciplinary projects organize around project or group boundaries as multiple disciplines contribute to a research problem. Group boundary crossing is likely to be a priority, but is unlikely to result in integrated outcomes. Interdisciplinarity, on the other hand, prioritizes these integrated efforts at the individual or actor level as knowledge from two or more disciplines is intertwined to generate a new outcome, such as a patent or research paper. This integration can take place by a single author building expertise in multiple disciplines or from a group of disciplined scientists working together. Note that interdisciplinary projects may have some interest in group boundary crossing as well, especially as they become more organized; however, our model focuses on primary motivations or priorities. Last, transdisciplinarity projects represent a fundamental challenge to existing structures of knowledge as they call for a more radical reorganization with high boundary crossing at both the group and the individual levels. Transdisciplinarity may serve to dramatically reduce the boundaries between the public and science (Frodeman 2014; Hadorn et al. 2010) or effectively eliminate the boundaries between groups previously organized by disciplines. Importantly, these states of organization are not permanent, but can be said to represent stages in the life course of a knowledge project. These stages indicate an empirically identifiable moment in time, but are characteristic of temporally embedded strategies by actors working on research problems. Again, these strategies of boundary work can lead to changes in organization. A life course perspective helps account for this temporal dimension by emphasizing the dynamics of knowledge production. Life course perspectives, borrowed from interdisciplinary work on health and aging, are resolutely longitudinal. In his overview of criteria for establishing a life course perspective, Mayer (2009) states that a key characteristic of life course research is a commitment to understanding change in human lives “over a long stretch of the lifetime” and not narrow windows of time. In the life course of knowledge projects, it is problematic to speak solely about brief—­and often arbitrary—­moments in time. While it may be empirically valuable to observe the consequences of the establishment of an interdisciplinary research center in the near term, the story of a

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knowledge project does not end here, but continues beyond these key moments. For example, as resources accrue around this hypothetical center it may assume the appearance locally of a discipline-­based department (e.g., awarding degrees and hiring faculty). Not a discipline in a clear sense, the center may also grow to discourage interdisciplinary interaction within the university as an attempt to preserve boundaries and scarce resources. A dynamic life course model acknowledges these shifting strategies and fates. Studying the life course of humans presents, on its face, a well-­bounded span: human lives begin and end at specific moments in time. The boundedness of knowledge projects is less specific. For example, whether emerging Frankenstein-­ like from the combination of previous knowledge projects or more subtly through fractalization (Abbott 2001), the starting point of a knowledge project is often more difficult to determine. This ambiguity presents a challenge to anyone studying the dynamics of knowledge projects. While our model does not resolve this issue, a life course approach to knowledge production requires some attention to start and end points. These points may be determined in numerous ways, but deserve careful consideration and clear articulation. In this way, the life course perspective that we propose may be more like how humans often describe their cultural versus their biological lives as a series of rebirths, new core turning points, and so forth. In a similar vein, those considering our perspective should look for these key moments of transition as identifiable temporal boundaries. Life course perspectives also are resolutely multidimensional: as Mayer (2009) writes, change is “studied across life domains.” When studying human lives, crossing domains means the consideration of multiple aspects of the lived experience from family to work to health. Within the life course of a knowledge project, crossing domains requires a theoretical position that allows for the consideration of multiple aspects of knowledge production from field-­level efforts at boundary maintenance to resource-­based struggles at the organization level to problem-­based contestation over ideas. This multidimensionality contrasts with prior work on interdisciplinarity, which tends to define knowledge production along one dimension, while subordinating others. As both Jacobs (2013) and Frodeman (2014) identify, for example, most prior definitions focus on epistemological—­or problem-­based—­dimensions of interdisciplinarity at the expense of resources or politics. In other words, work that romanticizes the combination of core disciplinary perspectives often misses the material struggle taking place between actors and organizations. Our theoretical approach embraces the multidimensionality of the life course perspective by accounting for knowledge production at the field, organization, and problem levels. By requiring a multilevel consideration, our model suggests that prior perspectives based largely on either resources or idea-­based considerations require integration for establishing a complete theoretical story about



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interdisciplinarity. While data limitations may focus attention on one dimension at the expense of others, critical approaches to interdisciplinarity should attempt to construct a multidimensional and dynamic lens for engaging knowledge production. In fact, the life course of some knowledge projects likely includes an uneasy relationship between stage alignment across those levels. For example, the tension that occurs when a rival interdisciplinary approach turns away from a problem-­based challenge to an established discipline to struggle over resources (e.g., neuroscientists demanding more lab space from their psychology colleagues or gender studies scholars arguing for departmental resources). To highlight the dynamics of knowledge production, we focus our attention on trajectories of knowledge projects. Trajectories are paths across the stages identified in Figure 6.1. These trajectories can follow many paths. For example, we can observe how a knowledge project moves from interdisciplinary research toward a disciplinary structure. Alternatively, we can observe projects that were solved and/or abandoned prior to the establishment of an organizational structure. Consider the case of American studies as described by Jacobs (2013; see also Dubrow 2011). The field’s trajectory begins with an epistemological complaint: the humanities in the United States were too wedded to European thought and offered little space to domestic cultural products, literary and otherwise. Initial boundary work focuses on expansion into the underexplored waters of American culture and an attempt to rehistoricize literary studies given the mid-­twentieth-­ century dominance of ahistorical New Criticism. Midcentury historians also had their complaints about the rigidity of the dominant approach to historical study that focused on key figures. Early work in American studies, thus, begins in a multidisciplinary vein. In ensuing decades, American studies expands to include some of the resources characteristic of disciplines—­including approximately 150 undergraduate degree programs by 1968—­yet it remained largely multidisciplinary (i.e., the degree programs drew faculty from the English and history departments). By the 1990s, even despite sluggish undergraduate enrollment, forty universities offered advanced degrees in American studies and six universities had stand-­alone PhD-­granting departments. The ideas-­based expansion resolved to organizational struggles for recognition within universities. Obviously this turn from epistemological to organizational concerns was only very modestly successful. At the field level, American studies has experienced more success with thriving journals and well-­attended annual meetings, preserving an interdisciplinary spirit. As Jacobs (2013, 186) writes, “In its efforts to transcend disciplinary boundaries, a fully interdisciplinary approach to academia would create a dizzying array of alternative maps of the intellectual terrain.” American studies obviously didn’t accomplish this type of radical restructuring; however, its organizational failures may have helped preserve its interdisciplinarity.

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The life course of American studies to this point includes a trajectory from a multidisciplinary approach to boundary expansion through organizational struggles that mirror disciplinarity to interdisciplinary field-­level structures. From an ideas-­based perspective the history of American studies may seem resolutely successful, while from a resources-­based perspective its failure to grow its campus presence may be deemed a failure. By encouraging multidimensionality, a life course lens allows for a more mixed story by accommodating both ideas-­and resource-­based explanations. As such, the case of American studies highlights the need for a dynamic, multidimensional approach to knowledge production. In the following section, we illustrate the multidimensional life course approach by focusing on the shifting boundaries of knowledge production for three cases: social sciences of religion, demography, and several specific problems within HIV/AIDS research.

Exemplar Trajectories To illustrate the efficacy of the model developed earlier, in the section that follows, we provide a number of empirical examples that demonstrate how particular projects have evolved through the various stages of boundedness that correspond to forms of (many) disciplinarities. While many trajectories are possible, we focus on several that we have identified as common in empirical work that has attempted to account for field-­and/or problem-­level dynamics. In the description, we noted how disciplinary projects can define and/or cross boundaries variously at the levels of fields, organizations, and problems. The examples to follow focus particularly on (1) field-­level trajectories that either (a) begin from or (b) end in disciplinary forms and (2) problem-­level trajectories for questions that are (a) resolved or (b) remain open. This leaves open the possibility for empirical investigations into how organizational structures evolve across the states identified by our model. While the parameters of the model would sufficiently allow for examining such questions, we simply don’t currently have data to investigate that level within the projects we draw from here. As such, we invite future researchers to investigate how readily the forms described in the model and the trajectories demonstrated in the examples outlined later are found in organizational settings. A Field That Began as a Discipline—­Social Sciences of Religion

The Society for the Scientific Study of Religion was founded in 1949 with its primary contributors coming from theology backgrounds (Newman 1974). While these founders were motivated by questions pertinent to theology and religious



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practice, they identified a need for perspectives from across the (social) sciences to best examine those questions (Newman 1974). To examine how readily these fundamentally interdisciplinary aims translated into practice, and how the structure of the field has evolved in the six decades since its formation, we draw on a corpus of 41,186 articles.3 With these articles, we construct a series of coauthorship networks, which draw ties between individuals based on their having written one (or more) articles together (Yan and Ding 2012). These coauthorship networks can then be analyzed to identify network communities that reveal groups of researchers who compose the boundaries between “invisible colleges” (or thought collectives) within research fields (Gmur 2003). The evolution of these clusters is estimated over the past six decades, starting just after the formation of the field. These clustering patterns evolve across the period revealing four distinct configurations; the shifts between these different structural patterns of the field happen to roughly correspond to individual decades. The earliest period was one of relatively little systematic organization, with mostly scattershot, disconnected research projects that had limited communication directly, who collaborates with whom, or indirectly, through sharing similar intellectual forebears. As the field grew, it rapidly evolved into a small but highly consolidated research field by the end of the 1970s. This signature is consistent with the ideological origins of the organization—­an interdisciplinary (social) scientific study of religion. This changed relatively rapidly however—­ through most of the 1980s, this initial enthusiasm appears to have waned somewhat, as the growth in the number of publications plateaued. In addition, the early consolidation was superseded by an increasing bifurcation within the literature. This separation into distinct communities was further entrenched into the 1990s, and even expanded to move from two into multiple communities. By the first decade of the 2000s, this segmentation became even greater, with those identifiable communities exhibiting even stronger boundaries between them. one of relatively entrenched A comparison of this last period—­ boundaries—­to a similar analysis of the whole of social sciences conducted by Moody and Light (2006) helps to make sense of the source of this evolution. The left panel of Figure 6.2 presents the bibliographic coupling network among papers published from 2000 to 2009 in the social sciences of religion corpus used here. The labels overlaid come from our examination of the authors on a sample of the most central papers within each cluster. What is revealed in comparing these clusters identified in our analysis (left panel of Figure 6.2) to those from Moody and Light’s (2006, Figure 1) analysis (right panel) is that the resulting clusters are remarkably similar in content and arrangement to those exhibited across all social science publications. That is, the field appears

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Figure 6.2. Structural comparison of the social sciences of religion (A) to all social sciences (B). Source: Social Sciences Citation Index.

to have evolved to currently mimic the general structure exhibited within the social sciences. The trajectory of the social sciences of religion is therefore one that progressed from initial disciplinary motivations, through relatively strong interdisciplinary collaboration, into a strongly multidisciplinary state where researchers from across the social sciences appear to be contributing to the content of the field, but doing so mostly within the confines of their own disciplinary homes. We don’t want to overstate the strength of these boundaries—­there is some interaction across these boundaries—­but the prevalent pattern here is one that is much more segmented than some of the other patterns that will be described later. One potential explanation for this emergent structure is the perspective offered by Smilde and May (2010) that our understanding of religion has matured into a “strong program” with distinctly measurable characteristics of religiosity that are increasingly understood to have identifiable causes and effects of importance to a range of social scientific interests. Fields That Evolved into a Discipline—­The Demography Example

Demography’s origins lie primarily in a number of population concerns—­ whether aims to control population growth globally or for particular populations, or to promote fertility or migration patterns to enhance the goals of particular societies (Hodgson 1991). From the outset, these concerns drew interest from a wide range of social sciences (Liu and Wang 2005) and were represented by



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core philosophical, mathematical, and statistical contributions (Kreager 1993). Given this orientation, one would expect clustering patterns that align with those key research questions—­and that is what is observed in the examples to follow—­clusters that primarily oriented, separately, around fertility, mortality, and migration research topics. This early problem orientation crossed disciplinary boundaries, and provided meaningful organization as participants in the field sought to address related sets of concerns. Of particular interest for our purposes here is how that organization subsequently changed as demographic questions increasingly coalesced into entrenched scientific questions, methods, and findings. Did it retain the early interdisciplinary organization, or evolve into some alternative pattern? As time passed, did the relationship(s) between each of the three key processes in demographic research change? To assess this, rather than the coauthorship networks used earlier, we combine two approaches using five decades of publications from five leading demography journals.4 First, we construct a series of bibliographic coupling networks, which draw ties between papers based on the degree of overlap between the references they cite (Yan and Ding 2012). These bibliographic coupling networks can then be analyzed in the same manner as outlined earlier to identify network communities. These communities reveal groups of research papers that rely on highly similar bodies of literature to construct their arguments, an alternative strategy for discovering “invisible colleges” within research fields (Gmur 2003). We then combine this network clustering with an analysis of the content of these research clusters. We do this by examining the frequency of the articles’ identified keywords within each cluster. For most of the observed period (1956–­2012), the field is structured primarily around three dominant clusters, which remain relatively closely aligned separately with the three focal demographic processes (i.e., fertility, mortality, and migration). Other topics periodically emerge (and wane), leading to a question of how the arrangement changes through time—­among both the three core topics and those other more short-­lived themes. The early period of these journals is marked by research on the “demographic transition,” or the effects of industrialization on demographic outcomes, which is marked by increasing life expectancy, decreasing infant mortality, and later declining fertility (Hodgson 1991) with a corresponding close overlap between research simultaneously engaging mortality and fertility. Over time, the strength of the boundaries between each of these three topics increases, but the boundaries never fully separate these literatures from one another: the central position in the literature moves from being relatively equally occupied by fertility and mortality topics to being occupied solely by fertility research. In addition, migration literature starts out as and remains more peripheral than either of the others throughout the period. Most of the secondary topics observed in the period (e.g.,

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family planning, environmental consequences of population growth, and HIV/ AIDS—­each prominently featuring within this sample of the demographic literature for slightly more than a decade) arise as topics that primarily bridge the fertility and mortality clusters and have little connection to the migration topic. This combination of patterns is consistent with an orientation that began largely around a single question (population growth) with a consolidated transdisciplinary arrangement. Over time, three primary research topics developed and retained distinct community boundaries. While occasional crossing occurred between them, bridging most likely took place via unaligned or secondary research topics. As a result, the field remains structured by primary communities: fertility, mortality, and migration. Along with this trajectory, there has been the founding of a professional association, journals, and even degree programs, leading some to identify demography as a discipline unto itself demarcated by the conceptual and methodological tools available for addressing these three topics (Morgan and Lynch 2001). The disciplinary boundaries around demographic research, however, remain relatively permeable with frequent contributions within those topically oriented clusters from scholars from a number of disciplines, including sociology, economics, public health, and others. This trajectory therefore raises the question of what would allow us to identify when transdisciplinary research fields (those with high individual-­and project-­level boundary crossing) coalesce strongly enough to qualify as a new discipline. This is a topic of much previous research (Bettencourt et al. 2008; Rojas 2010), and something we think would be an important integration for future applications of the model we develop here. Resolved Problem-­Oriented Questions in HIV/AIDS Research

In much of the literature extolling the potential benefits of interdisciplinary work, a key assumption is that approaches drawing from ideas spanning disciplinary boundaries will be more likely to generate solutions (Boyack et al. 2005; National Academy of Sciences et al. 2005). In some cases, this has proven to be correct. Drawing from our work on HIV/AIDS research (adams and Light 2014; Light and adams 2010), we identify a few topics that incorporate key resolved questions, and describe the trajectories of those cases across the model described earlier. Our contention is that if we map out several of these trajectories, it can allow us to shift the conversation from an oversimplified question of “Does interdisciplinary research benefit solving problems?” to a more theoretically useful question of “What patterns of disciplinary boundary crossing allow for more efficient problem solving?” In other words, these help us focus on the process(es) that underpin successes rather than on the successes themselves. This is helpful not only for making sense of the



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focal questions themselves, but also for thinking about how these models can potentially be of use to other cases. Few recent research fields have witnessed a more public and urgent call to interdisciplinarity than HIV/AIDS research. HIV/AIDS was initially conceived as a problem with important contributions to be made by medical practitioners, epidemiologists, policy makers, and activists, among other constituencies. In the case of HIV/AIDS research, two key empirical developments with wide-­ranging implications have been the development of nevirapine—­a single-­dose treatment used to prevent mother-­to-­child transmission (PMTCT) of HIV at birth, and the development of strategies for testing for HIV antibodies. In this case, like with the demography example, we align bibliographic network clusters with topics. Instead of using keywords for identifying the content, we use examples from our work elsewhere that use topic models (adams and Light 2014; Light and adams 2010). This approach consists of a class of techniques that locate structure in unstructured text corpora (Griffiths and Steyvers 2004; Steyvers and Griffiths 2007) by “reverse engineering” the writing process to uncover latent themes within the corpus that underlie their generative processes. Topic models locate patterns within text data based on how words overlap in texts, in this case scientific abstracts. While the math operating behind topic models is quite complicated, the logic is simple: some words appear together with greater likelihood than others. By identifying these distributions of words—­or topics—­we can develop an overview of what scientists have written about in our collection of abstracts. By using unstructured data, topic models include more fine-­grained information than using other bibliometric data, such as keywords. While many methods are needed to successfully explore knowledge projects, topic models may be particularly important for locating emergent themes as constructed by scientists themselves without relying on cumbersome predefined or user-­generated coding schemes.5 Within the topic models of HIV/ AIDS abstracts, PMTCT and the development of testing assays were each identified among thirty primary topics present in the first twenty years of published work in AIDS and JAIDS, two many-­disciplined journals on HIV/AIDS. The PMTCT of HIV became a focus in the field at a relatively early period in the field of HIV/AIDS research, especially as work increasingly focused on Africa (which became an overrepresented topic in the field beginning in the late 1990s). Single-­dose nevirapine, which became available in the early 2000s, reduces the likelihood of mother-­to-­child transmission at birth by over 40 percent (Guay et al. 1999). As such, describing how the PMTCT literature progressed within the HIV/AIDS field can provide an informative case. By extracting only those articles that engaged the PMTCT topic out of the bibliographic coupling networks described earlier, we examined the community patterns of this literature through time, and illustrate its evolution in Figure

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Figure 6.3. Consolidation in prevent mother-­to-­child transmission research. Source: ISI Web of Science.

6.3 (nodes differ by section labels from the journal—­basic science, clinical, social/epidemiological sciences). What this topic demonstrates is an early period in which there was a single consolidated research community, but within that community, researchers were represented from a wide range of disciplinary backgrounds. This is consistent with the pattern of transdisciplinarity as described earlier—­virtually boundaryless work attempting to collectively solve a problem of practical importance. Over time, this pattern changed—­most drastically, in the periods after the availability of nevirapine (2001 and following). Namely, while this topic remains relatively well consolidated in a single cluster, some minor segmentation arises (see split between the north and western portions in the 2008 slice), and this consolidated cluster became increasingly dominated only by researchers from the social science.6 This is a move that is increasingly consistent with a more disciplinary



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organization—­perhaps consistent with the practical concern of moving from attempting to identify how to prevent MTCT to a focus on removing barriers to implementing that identified solution. In sum, the trajectory of PMTCT is one of high transdisciplinary integration that led to the discovery of nevirapine, which resolved a particular aspect of the problem and has lead subsequently to a topic largely addressed within disciplinary confines. Research on the development of reliable testing assays for identifying when someone has HIV was a central question in the field that arose almost immediately after the identification of HIV as the putative causal agent of AIDS. The literature on this topic within the journals AIDS and JAIDS followed a similar structural pattern to that of PMTCT described earlier in that it arose and remained consolidated in a single research community, until reliable methods were available for wide use (Delaney et al. 2006). However, this literature differed from the PMTCT trajectory in two important ways. First, the consolidated cluster associated with addressing this question had a remarkably variable composition, represented by bench scientists attempting to develop the actual assays, and social scientists more concerned with the potential usability of the developed solutions: this case was marked more by interdisciplinary organization early in the observed period. Once a solution was identified, the contribution of this research topic to the corpus became substantially diminished and almost eliminated completely by the late 2000s. Open Problem-­Oriented Questions in HIV/AIDS Research

Not all forms of many-­disciplined science are created equally. While the differences between the various forms described earlier have been well documented, it’s also plausible to identify problems that exist in similar states identified by the model that exhibit differing levels of success in addressing the questions they raise. As was the case previously, our question at this stage is not necessarily to equate particular forms and/or trajectories as being more/less likely to generate successes or failures for problem-­oriented science. We a­­ ren’t to that stage yet. Instead we are simply describing problems that exhibit particular trajectories and—­for now—­seeking to identify commonalities and/or differences between those. In this section, we continue to focus on examples from our work on HIV/AIDS aligning bibliographic clustering networks and topic models, but instead turn to trajectories that are observed for questions that remain, as yet, unresolved. In particular, we examine the trajectories of topics pertaining to three research areas: drug failures, especially those that arise from the development of drug resistance, the pursuit of an HIV vaccine, and efficacy of antiretroviral treatment. These three examples exhibit substantially different trajectories.

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Figure 6.4. Segmented communities in drug resistance HIV research, 2004. Source: ISI Web of Science.

The drug resistance topic we identified from the AIDS/JAIDS corpus is a topic that does not have any substantial contribution in the literature until the early 2000s. Once it arises, it does so with representation in two separate network communities. Those two communities remain prominent and separated from one another throughout the 2000s. Moreover, the split between these two communities appears to be more aligned with disciplinary divisions than with problem-­oriented differentiation. As in the example presented in Figure 6.4, one of the communities addressing this topic is more closely aligned with the bench/ basic sciences, while the other is more closely aligned with clinical-­based research. This alignment fluctuates moderately across the observed period, but the general pattern is one of dominance within those segmented clusters that differentiate more along paradigmatic (disciplinary) orientations throughout. Drug resistance research has been resolutely multidisciplinary within AIDS and JAIDS.



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Research on vaccines is marked by a substantially different structural signature from the drug resistance literature, but exhibits a similarly remarkable stability in that structure over time. Vaccine development is a research topic that features relatively prominently throughout the history of the journals AIDS and JAIDS. Moreover, it is a literature that appears relatively consolidated into a single community across that entire period. Where it differs is in the composition of that community—­being dominated almost exclusively by basic science research—­predominantly virologists who specialize in vaccine research. HIV vaccine development appears to have remained a disciplinary pursuit for three decades. Research on the efficacy of antiretroviral treatments exhibits substantially different trajectories than either of the other “open” questions described earlier. First, like drug resistance research, it is a relatively late-­breaking research topic (showing little presence in the early to mid-­1990s). Once it arises, however it does so within a relatively consolidated single community—­mostly associated with bench sciences (likely the same researchers who were concerned with developing the treatments in the first place). Over time, this research topic has increasingly segmented into coverage by two separate research communities. Those separate communities are increasingly (in the mid to late 2000s) represented by clinical research concerned with evaluating the effectiveness of treatment regimens, which is separate from more epidemiological research that is concerned with identifying differential predictors (particularly the social determinants) of the success/failure of those treatment regimens. In sum, over time this research topic has increasingly been further segmented into differential primary research questions, which are increasingly independently addressed by researchers from different disciplinary perspectives; that is, it has evolved from a mostly disciplinary approach to a more multidisciplinary orientation.

Conclusions and Future Directions Knowledge production does not follow a predetermined path. Prior theoretical attention to interdisciplinarity assumes many-­disciplined projects follow one of two trajectories. Interdisciplinary utopians see interdisciplinarity as the skeleton key to solving complex, pressing problems, while skeptics worry that interdisciplinarity obscures resource-­based contests. Our approach attempts to avoid these overly determined trajectories by building dynamics into the core of knowledge production. When viewed from a life course perspective, stages of research such as disciplinarity and interdisciplinarity are not static but subject to change. As our examples illustrate, knowledge projects may occur along different trajectories with shifting boundaries as the projects mature and as problems resolve

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or remain open. From a field-­based perspective, the social scientific study of religion grew out of disciplines—­religious studies and theology—­through a process of expansion as the social sciences grew in prominence. However, many social sciences also saw religion as a core element of their areas of study and eventually tried to expand into the subject as well. The result is a current state of multidisciplinarity, which mirrors the broader structure of social science. Demography, on the other hand, began as a problem-­based knowledge project with a relatively unbounded structure and engagement by numerous disciplines and perspectives. As resources began to accumulate and questions narrowed, demography—­as a field—­grew into a nascent, yet powerful discipline with somewhat porous boundaries. Focusing on a different dimension of knowledge production, our illustrative examples drawn from HIV/AIDS research describe how boundary work progresses within a field at the problem level. Mother-­to-­child-­transmission of HIV was a core concern that initially engaged a flurry of transdisciplinary research. The discovery of nevirapine effectively “solved” this problem, and remaining ancillary questions are solved mostly within disciplines. On the other hand, drug resistance remains an open or unresolved question. Research on this question begins in a disciplinary fashion and moves to a multidisciplinary one, perhaps as a result of the difficulties in advancing knowledge on resistance. Specialization with highly complex biomedical problems may encourage multidisciplinary, but hinder interdisciplinary research. These trajectories serve as several possibilities for the paths that knowledge work can assume in the model that we outline, yet this is not an exhaustive list. Evaluations of knowledge projects should continue to explore the trajectories of successful and failed research programs. A key tension within prior work on interdisciplinarity examines whether evaluations of interdisciplinarity should focus on ideas-­based or organizational concerns. Our multidimensional focus recognizes that these are not mutually exclusive positions. Rather, epistemological and organizational concerns are dimensions of a complex system of boundary work where actors attempt to both solve problems and secure resources. In other words, an institutional approach to understanding is not inherently incompatible with an idea-­based approach. By seeing these as dimensions of a dynamic system, our theoretical model becomes one basis for evaluating these competing claims. Yet, our current examples draw from the field at the level of problems, at the expense of parallel analyses at the organizational level. A more complete understanding requires the pairing of organizational—­and specifically resource-­based—­data with problem-­and field-­ level data. Fortunately, methodological tools increasingly parallel the multidimensional and dynamic theory that we propose. Advanced techniques in computer and information science and methods for the dynamic analysis of social networks



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promise to change the landscape of how we empirically model the organization of knowledge. Those interested in building critical tools for evaluating when interdisciplinarity works and when it fails have a robust theoretical framework and the appropriate methodological tools to work with. Given the entrenched and powerful interests involved, this combination must be leveraged to generate a successful empirical approach to understanding interdisciplinarity. notes 1  Following Jacobs and Frickel (2009, 45), we define multidisciplinarity as the “contribu-

tions of two or more fields to a research problem,” interdisciplinarity as the “integration of knowledge originating in two or more fields,” and transdisciplinarity as “knowledge produced jointly by disciplinary experts and social practitioners.” Disciplinarity, on the other hand, is the institutionally bounded, modal form of research production on academic campuses. 2   Note that Freeman (2004) and others try to guide the reception of physics within the networks community and not eliminate cross-­pollination. In fact, Freeman encourages bridge building between these communities. Nonetheless, as an act of turf defense, the resistance of cooptation captures a more modest form of expulsion. 3   The corpus included all articles published from 1960 to 2009 in the Social Sciences Citation Index that included any of the following as a keyword (an asterisk denotes a wildcard search term, such that church* includes church, churches, etc.): church*, relig*, secular*, god*, spiritual*, denomination*, congregat*, catholic*, protestant*, faith*, fundamentalis*, clerg*. This list was generated from identifying the top one hundred keywords used in two social science journals that focus on religion: Journal for the Scientific Study of Religion and Review of Religious Research. 4   Those journals represent a wide range of general research on demography (2012 impact factor in parentheses): Demography (2.305), Population Research and Policy Review (0.610), Population Studies (1.147), Population and Development Review (1.998), and Demographic Research (1.047). Due to this sampling, the period examined covers 1956 to 2012. 5   For an overview of the use of topic models in sociology, see Mohr and Bogdanov (2013). 6   All visualizations require some visual reduction. Here, we included only edges in the network that are especially strong (mean plus two standard deviations). The increasing dominance of the social sciences is especially clear in the full analyses from which these visualizations are extracted; see Appendices S3 and S4 in adams and Light (2014) for the full details. references

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Blei, David M., Andrew Y. Ng, and Michael I. Jordan. 2003. “Latent Dirichlet Allocation.” Journal of Machine Learning Research 3: 993–­1022. Boyack, Kevin W., Richard Klavans, and Katy Börner. 2005. “Mapping the Backbone of Science.” Scientometrics 64 (3): 351–­374. Campbell, Donald T. 1969. “Ethnocentrism of Disciplines and the Fish-­Scale Model of Omniscience.” In Interdisciplinary Relationships in the Social Sciences, edited by M. Sherif and C. W. Sherif, 328–­348. Chicago: Aldine. Delaney, Kevin P., Bernard M. Branson, Apurva Uniyal, Peter R. Kerndt, Patrick A. Keenan, Krishna Jafa, Ann D. Gardner, Denise J. Jamieson, and Marc Bulterys. 2006. “Performance of an Oral Fluid Rapid HIV-­1/2 Test: Experience from Four CDC Studies.” AIDS 20 (12): 1655–­1660. doi:10.097/01.aids.0000238412.75324.82. Dubrow, Joshua Kjerulf. 2011. “Sociology and American Studies: A Case Study in the Limits of Interdisciplinarity.” American Sociologist 42: 303–­315. Freeman, Linton C. 2004. The Development of Social Network Analysis: A Study in the Sociology of Science. Vancouver: Empirical Press. Frodeman, Robert. 2014. Sustainable Knowledge: A Theory of Interdisciplinarity. New York: Palgrave Macmillan. Gieryn, Thomas F. 1999. Cultural Boundaries of Science: Credibility on the Line. Chicago: University of Chicago Press. Gmur, Markus. 2003. “Co-­citation and the Search for Invisible Colleges: A Methodological Evaluation.” Scientometrics 57 (1): 27–­57. Griffiths, Thomas L., and Mark Steyvers. 2004. “Finding Scientific Topics.” Proceedings of the National Academy of Science 101 (S1): 5228–­5235. Guay, Laura A., Philippa Musoke, Thomas Fleming, Danstan Bagenda, Melissa Allen, Clemensia Nakabiito, Joseph Sherman, Paul Bakaki, Constance Ducar, Martina Deseyve, Lynda Emel, Mark Mirochnick, Mary Glenn Fowler, Lynne Mofenson, Paolo Miotti, Kevin Dransfield, Dorothy Bray, Francis Mmiro, and J. Brooks Jackson. 1999. “Intrapartum and Neonatal Single-­Dose Nevirapine Compared with Zidovudine for Prevention of Mother-­to-­Child Transmission of HIV-­1 in Kampala, Uganda: HIVNET 012 Randomised Trial.” Lancet 354 (9181): 795–­802. Hadorn, G. Hirsch, Christian Pohl, and Gabriele Bammer. 2010. “Solving Problems through Transdisciplinary Research.” In The Oxford Handbook of Interdisciplinarity, edited by J. T. Klein and C. Mitcham, 431–­452. New York: Oxford University Press. Hodgson, Dennis. 1991. “The Ideological Origins of the Population Association of America.” Population and Development Review 17 (1): 1–­34. doi:10.2307/1972350. Jacobs, Jerry A. 2013. In Defense of Disciplines: Interdisciplinarity and Specialization in the Research University. Chicago: University of Chicago Press. Jacobs, Jerry, and Scott Frickel. 2009. “Interdisciplinarity: A Critical Assessment.” Annual Review of Sociology 35: 43–­65. Klein, Julie Thompson. 1990. Interdisciplinarity: History, Theory, and Practice. Detroit: Wayne State University Press. Kohler, Robert E. 1982. From Medical Chemistry to Biochemistry: The Making of a Biomedical Discipline. Cambridge: Cambridge University Press. Kreager, Philip. 1993. “Histories of Demography: A Review Article.” Population Studies 47 (3): 519–­539. doi:10.1080/0032472031000147286. Lamont, Michele, and Virag Molnár. 2002. “The Study of Boundaries in the Social Sciences.” Annual Review of Sociology 28: 167–­195.



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Leydesdorff, Loet, and Thomas Schank. 2008. “Dynamic Animations of Journal Maps: Indicators of Structural Changes and Interdisciplinary Developments.” Journal of the American Society for Information Science and Technology 59 (11): 1810–­1818. Light, Ryan, and jimi adams. 2010. “The Spread of HIV/AIDS Research: Topic Structures in AIDS and JAIDS, 1988–­2008.” Paper presented at Sunbelt XXX, annual meetings of the International Network for Social Network Analysis, Riva del Garda, Italy. Liu, Zao, and Chengzhi Wang. 2005. “Mapping Interdisciplinarity in Demography: A Journal Network Analysis.” Journal of Information Science 31 (4): 308–­316. Mayer, Karl Ulrich. 2009. “New Directions in Life Course Research.” Annual Review of Sociology 35: 413–­433. Mohr, John W., and Petko Bogdanov. 2013. “Introduction: Topic Models: What They Are and Why They Matter.” Poetics 41: 545–­569. Moody, James, and Ryan Light. 2006. “A View from Above: The Evolving Sociological Landscape.” American Sociologist 37 (2): 67–­86. Morgan, S. Philip, and Scott M. Lynch. 2001. “Success and Future of Demography: The Role of Data and Methods.” Annals of the NY Academy of Sciences 954: 35–­51. National Academy of Sciences, National Academy of Engineering, Institute of Medicine, and Committee on Facilitating Interdisciplinary Research. 2005. Facilitating Interdisciplinary Research. Washington, DC: National Academies Press. Newman, Mark. 2008. “The Physics of Networks.” Physics Today 61: 33–­38. Newman, William M. 1974. “The Society for the Scientific Study of Religion: The Development of an Academic Society.” Review of Religious Research 15 (3): 137–­151. Rojas, Fabio. 2010. From Black Power to Black Studies: How a Radical Social Movement Became and Academic Discipline. Baltimore: Johns Hopkins University Press. Smilde, David, and Matthew May. 2010. “The Emerging Strong Program in the Sociology of Religion.” SSRC Working Papers. Steyvers, M., and T. Griffiths. 2007. “Probabilistic Topic Models.” Handbook of Latent Semantic Analysis 427 (7): 424–­440. Taylor, Mark. 2010. Crisis on Campus: A Bold Plan for Reforming Our Colleges and Universities. New York: Knopf. Yan, Erija, and Ying Ding. 2012. “Scholarly Network Similarities: How Bibliographic Coupling Networks, Citation Networks, Cocitation Networks, Topical Networks, Coauthorship Networks and Coword Networks Relate to Each Other.” Journal of the American Society for Information Science and Technology 63 (7): 1313–­1326.

7 ◆ Disciplinary and Interdisciplinary Change in Six Social Sciences A Longitudinal Comparison S c ot t F r i c k e l a n d A l i O. I l h a n

Disciplines are academic fields of study whose homogeneous organization in universities and colleges generally takes the form of autonomous departments (Abbott 2001). Disciplines also control labor markets for faculty, nearly always hiring from within their own field (Turner 2000). By contrast, interdisciplines are academic fields of study whose organization is heterogeneous, consisting of a mix of departments, subunits within departments, or extra-­departmental units such as programs, centers, or institutes (see Klein 1990, 186–­189; Jacobs and Frickel 2009, 53–­54) and, unlike disciplines, interdisciplines do not control faculty labor markets. Instead, they tend to hire, absorb, or link to faculty trained in other fields. The heterogeneity of faculty organization and diversity of faculty expertise mark an academic unit, the curriculum that unit develops, and the degrees students earn by taking those courses as “interdisciplinary.” Given such differences, do interdisciplines and disciplines grow in different ways too? Or are they shaped instead by similar kinds of organizational and social influences? Answers to such basic questions remain elusive. For despite wide-­ranging scholarly efforts to better understand the dynamics of interdisciplinary activities and their social, professional, and epistemological impacts (Frodeman et al. 2010), few existing studies employ robust, longitudinal data to systematically compare disciplinary and interdisciplinary processes and outcomes ( Jacobs 2013). To date researchers have conducted longitudinal case studies of individual disciplines or interdisciplines (Frank et al. 1994; Jacobs 2013; Rojas 2006) 148

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or have compared different interdisciplinary fields (Brint et al. 2009; Jacobs 2013; Olzak and Kangas 2008; Turk-­Bicakci 2007). Such studies yield important knowledge, but shed little light on whether and how disciplinary and interdisciplinary change is related. In the spirit of this book’s larger goal of bringing comparative research to bear on questions about interdisciplinarity, this chapter takes a small but important step toward a more systematic understanding of the relationship among disciplinary and interdisciplinary fields. Part of a larger ongoing study to understand disciplinary and interdisciplinary change in the U.S. academy over the past three decades, this study investigates the organizational and social factors that have shaped patterns of change among three traditional social science disciplines that we compare with three historically related interdisciplines: geography/ urban studies, political science/international relations, and sociology/criminology.1 We use a unique longitudinal data set to identify the impact of organizational, demographic, and economic factors on the distribution and share of undergraduate degrees in these six fields during the period 1978 to 2008. Results from lagged random effects regression analyses indicate that organization size accounts for the largest variation in temporal change among both types of fields, despite real and persistent organizational differences marking disciplinary and interdisciplinary fields. While preliminary, these findings add complexity to current debates about the nature and role of interdisciplinary organization of higher education and suggest important avenues for future research.

Historical Background The first major expansionary wave of the U.S. higher education system began in the late nineteenth century and produced the distinctly American PhD-­granting research university, which organized research and teaching around discipline-­ based academic departments (Geiger 1986). Initially centered on the humanities and natural sciences, university and college campuses began adding social science departments during the early decades of the twentieth century (Frank and Gabler 2006). Mostly imported from Europe, the disciplines of geography, political science, and sociology were firmly established on U.S. academic soil by 1930 (Ross 1991; Martin 2005). Once institutionalized, these fields (along with anthropology, economics, and psychology) established the disciplinary division of general social knowledge that remains largely intact today. Criminology, international relations, and urban studies are interdisciplines that also trace their origins in the U.S. academy to the early twentieth century (Bowen et al. 2010; Schmidt 1998; Short and Hughes 2007), although their broader institutionalization came a few decades later, in the 1930s to 1960s.2 Developed during a period of system expansion characterized by rapid increases

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disciplines and interdisciplinarity

in research funding and college enrollments (Geiger 1993), these interdisciplines tended to be homegrown rather than imported in the sense that they occupied organizational niches and advanced research agendas guided by domestic intellectual currents and politics.3 They also enjoyed support from elite U.S. institutions such as the Social Sciences Research Council, the Ford and Rockefeller Foundations, and agencies of the federal government (Abbott 2001; Calhoun and Rhoten 2010; Bowen et al. 2010). And, in contrast to the disciplinary production of general theoretical knowledge, these interdisciplines aimed to produce problem-­centric knowledge oriented toward investigating complex social issues—­rising crime rates, municipal governance, geopolitical security, and the like (Abbott 2001). In this way, they complemented mainstream academic sensibilities and accommodated the changing needs for social research, taking their place within or beside existing departments without seriously threatening established disciplinary identities or resource pools. The six fields in our study thus date their initial emergence in the early twentieth century and their fuller institutionalization in proximate time periods covering the first half of the twentieth century (1900s–­1930s for the disciplines; 1930s–­1960s for the interdisciplines). This institutional history helps us establish the logic of our comparative analysis, which covers the last three decades (1978–­ 2008) and focuses on similarities and differences across the two groups of older social science fields—­a selected subset of original core disciplines and a selected subset of first generation interdisciplines.

Contemporary Organization of the Six Inter/Disciplines To determine whether these six inter/disciplines cluster into two distinct sets at the organizational level, we used 2008 Integrated Postsecondary Education Data Survey data to generate random samples of academic units from each of the six fields. We then consulted academic websites and publicly available faculty curricula vitae to calculate the proportion of units identified as academic programs, which other studies have shown to be more weakly resourced and less stable than academic departments (Brint et al. 2009). We also calculated the proportion of faculty in each unit holding PhDs in “native” or self-­similar fields, following Turner (2000). Results appear in Table 7.1 and show that the three interdisciplines in our study are far more likely than the disciplines to be institutionalized as academic programs rather than as departments. For example, 73 percent of criminology units are organized as programs, while no sociology units are organized this way, a pattern that is even more pronounced for international relations and urban studies. In addition, interdisciplines are far less likely than disciplines to be composed of faculty holding native PhDs. For example, among faculty in criminology,

Disciplinary and Interdisciplinary Change table 7.1

Organization Type and Faculty Composition of Select Social Science Disciplines and Interdisciplines Discipline

Interdiscipline

Sociology

Political science

Geography

Criminology

International relations

Urban studies

50 (5.1)

60 (5.9)

32 (11.3)

30 (29.5)

30 (8.0)

30 (28.5)

0 (0.0)

4 (6.6)

0 (0.0)

22 (73.0)

24 (80.0)

23 (77.0)

Total faculty in all unitsa

291

433

249

191

223

272

Percentage of faculty with native PhDs

87.3

80.6

77.5

25.7

4.9

2.2

Sample size, n (%) Academic units organized as programs, n (%)

a

151

Includes only faculty with publicly available curricula vitae.

only 25 percent hold criminology PhDs compared to 87 percent of sociology faculty, and we see this same pattern in international relations and urban studies. The uniformity of these findings justifies our decision to analyze the six fields as two distinct disciplinary and interdisciplinary sets of cases.4 In the next section we turn to existing theoretical work to help us explain these organizational differences and to develop hypotheses.

Theoretical Background and Hypotheses Our efforts to understand relational change among these six social science fields begin with a theoretical framework developed by Andrew Abbott in his book Chaos of Disciplines (2001). Abbott’s study represents one of the few efforts to develop a theory of inter/disciplinary relations in the U.S. higher education system. While the arguments Abbott developed in that study have attracted broad critical assessment,5 to our knowledge the theoretical claims he makes in this work have not been tested empirically. We take advantage of the unique longitudinal nature of our data set to engage his theory directly and offer some refinement to the theory as it relates to American social sciences. According to Abbott, since becoming established in the late nineteenth century, discipline-­based departments have operated as stabilizing forces within higher education. He identifies two mechanisms that create and reinforce this organizational stability, the PhD labor market and the undergraduate major, both of which have historically been tied to academic departments in the natural sciences, social sciences, and humanities. These “disciplinary departments are the essential and irreplaceable building blocks of American universities,”

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disciplines and interdisciplinarity

Abbott (2001, 128) observes, because they organize both the demand for discipline-­based education from students and the supply of faculty to teach those courses. Interdisciplines are a direct consequence of this disciplinary structure. According to Abbot, they appeared on the scene “almost immediately” (2001, 132) and continue to do so by fulfilling more short-­lived, problem-­focused curricular and research interests of students and faculty. These interdisciplinary projects pose little threat to the long-­term stability of disciplinary departments, in part because faculty research interest in different interdisciplinary problems is highly fluid, and in part because the PhD labor market for academic faculty is controlled by the disciplines, which puts a check on the demand for interdisciplinary PhDs. Reinforced by the primary disciplinary structure of academic departments, this standing wave of interdisciplinarity is an entrenched secondary feature of the larger system (Abbot 2001, 134). Abbott’s framework helps explain the historical stability of the two-­tiered organizational structure, but tells us little about how system-­level change occurs. Research considering how organizational environments influence the presence or absence of different fields at different types of universities and colleges offers important insight. For example, researchers have shown that organizational capacity conditions opportunities for interdisciplinary research and education ( Jacobs 2013; Rojas 2006). More specifically, Brint et al. (2009) found that academic institutions that are larger and wealthier are more likely to provide an ensemble of undergraduate interdisciplinary programs than smaller and poorer ones. It takes money to create and maintain programs, and each additional program in a university’s portfolio represents a smaller relative investment for larger and wealthier schools than for smaller and poorer schools. Because on average private universities and colleges tend to control more resources than public ones of comparable size (National Association of College and University Business Officers and Commonfund Institute 2012), we also expect that private schools will be more likely to nurture interdisciplines. Following Abbott’s argument that interdisciplines are structurally related to disciplines, we believe this logic also extends to disciplinary units. Thus, our first set of hypotheses: H1a. Organization size will positively affect the presence of disciplinary and interdisciplinary units on campus. H1b. The size of organization budgets will positively affect the presence of disciplinary and interdisciplinary units on campus. H1c. Private funding structure will positively affect the presence of disciplinary and interdisciplinary units on campus.

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153

Presence of an inter/discipline represents an organization-­level mechanism driving field-­level change. For example, as more sociology departments are added to individual colleges and universities, the more sociology grows as a disciplinary field. Yet the mere presence of sociology departments tells us little about whether the discipline is robust or anemic relative to other fields inhabiting those same colleges and universities. Knowing this requires measuring the share of bachelor’s degrees claimed by various academic units across different campuses. Share is complementary to presence. It tells us that the more students that sociology departments claim as their own relative to other campus units, the more robust sociology grows as a field, especially in the context of an expanding system (see Ilhan 2013). Like the presence of academic units, the share of the total bachelor’s degrees claimed by those units is also likely to be affected by that university’s size, budget, and private funding status. But unlike presence, which we think will be positively affected by these three organizational characteristics, we believe their effect on share will be negative. This is because, first, as the number of departments and programs on campus increases, ceteris paribus, the level of resources available to individual departments or programs decreases. Moreover, while maintaining an existing academic unit requires little sustained administrative attention or new justification for already-­committed expenditures, actively nurturing a department or program by adding new faculty lines and new curricula to attract undergraduate majors demands continued attention and justification of changing commitments. Under these conditions, some departments and programs will be nurtured and grown at the expense of others. We think these conditions will be most acute at private universities that tend to concentrate investments in liberal arts fields (Brint et al. 2012). And, again following Abbott (2001), we expect that share will influence disciplines and interdisciplines in similar ways. These ideas inform our second set of hypotheses: H2a. Organization size will negatively affect the share of disciplinary and interdisciplinary units on campus. H2b. The size of organization budgets will negatively affect the share of disciplinary and interdisciplinary units on campus. H2c. Private funding structure will negatively affect the share of disciplinary and interdisciplinary units on campus.

Data and Sample This study employs data from the Integrated Postsecondary Education Data Survey (IPEDS; 1986–­2008) and the Higher Education General Information Survey

154

disciplines and interdisciplinarity

(HEGIS; 1968–­1985). The two surveys contain historical information on more than 1,100 academic fields of study at more than 6,700 degree-­and non-­degree-­ granting colleges and universities in the United States (http://nces.ed.gov/ ipeds/about/). We merged and aggregated IPEDS/HEGIS data with data from the U.S. Census Bureau and U.S. Bureau of Labor Statistics to construct a longitudinal data set that offers a unique combination of field-­, organization-­, and state-­level information that is well suited for investigating historical change among disciplines and interdisciplines. Our population of interest is four-­year U.S. colleges and universities. Because the change processes we are interested in overwhelmingly occur in those organizations that hold Carnegie Foundation classifications, we excluded those that do not from analysis. Missing data problems led us to also exclude four-­year schools with fewer than two hundred students, religious seminaries, American Indian tribal colleges, and schools located in nonstate U.S. territories. The data for lagged independent variables were collected biannually from 1974 to 2004; dependent variables were collected biannually from 1978 to 2008. Sample size across the thirty-­year study period varies, ranging from 1,388 (in 1978) to 1,631 (in 2008). In total, the data set includes 1,731 universities and colleges and 27,696 university-­years.

Aggregate Patterns Figure 7.1 describes the growth of the six fields as a percentage of degree-­active units within all four-­year universities and colleges (to control for system-­wide expansion). It tells us whether fields are gaining or losing departments and programs and reveals two dominant patterns. The first pattern is that these disciplines are one or more orders of magnitude larger than their sibling interdiscipline, reflecting the second-­tier structure of interdisciplines that Abbott’s theory posits. The second notable pattern is that growth in all six fields is highly stable over the entire study period. While some fields grow marginally larger and others grow marginally smaller, overall there is very little net change across the entire period of study for any of these fields. This pattern also seems to conform with the expectations of Abbott’s model, in which temporal stability, not dynamism, characterizes the academic system. We can also measure field-­level growth by calculating a field’s share of bachelor’s degrees conferred by their home universities and colleges. This tells us about the ability of fields to attract undergraduate students relative to other fields competing for similar organizational resources. Because the number of degrees that academic units confer annually factors into administrative decisions governing resource allocation at universities and colleges (Liefner 2003; Weerts and Ronca 2006), fields that produce proportionately fewer degrees are comparatively less likely to benefit

Disciplinary and Interdisciplinary Change

155

Percent of all four-year universities and colleges

80 70 60 50 40 30 20 10

Sociology

Criminology

Political Science

International Relations

Geography

Urban Studies

2008

2006

2004

2002

2000

1998

1996

1994

1992

1990

1988

1986

1984

1982

1980

1978

0

Figure 7.1. Degree-­active units in select social science disciplines and interdisciplines, 1978–­2008. Source: Integrated Postsecondary Education Data Survey and Higher Education General Information Survey files, 1978-2008.

from administrative funding and reorganization decisions than fields that produce more degrees. By aggregating the annual percentage of degrees and dividing by the total number of universities and colleges conferring degrees in each year, we gain a longitudinal view of a field’s proportionate growth, as shown in Figure 7.2. Here we see additional evidence that social science disciplines and interdisciplines are more similar than different. Both kinds of fields share roughly equivalent proportions of degrees, and these shares do not change appreciably over time. Instead, as

156

disciplines and interdisciplinarity

Percentage in schools with degree active units

8 7 6 5 4 3 2 1

Sociology

Criminology

Political Science

International Relations

Geography

Urban Studies

2008

2006

2004

2002

2000

1998

1996

1994

1992

1990

1988

1986

1984

1982

1980

1978

0

Figure 7.2. Share of bachelor’s degrees in select social science disciplines and interdisciplines, 1978–­2008. Source: Integrated Postsecondary Education Data Survey and Higher Education General Information Survey files, 1978-2008.

with field size, field share remains basically stable over the study period for all six fields. The uniformity of this pattern suggests that none of these U.S. social science fields are becoming stronger or weaker in their core mission to educate undergraduate students relative to degree programs in other academic domains. More importantly, the patterns shown in Figures 7.1 and 7.2 suggest that scholars’ claims about the ubiquitous growth and vitality of interdisciplines may be overstated—­at least in the social sciences.

Disciplinary and Interdisciplinary Change

157

In the next section we examine these dynamics at the institution level, where we investigate the influence of organizational and social factors on the presence and share of bachelor’s degrees in our six selected inter/disciplines.

Regression Analysis Dependent Variables

Dependent variables are field presence and share of undergraduate degrees. Field presence is operationalized as a dichotomous measure of whether, for each organization in our sample, one or more bachelor’s degrees were conferred in a given field (1 = yes, 0 = no). This variable provides a measure of contextual and institutional factors influencing the distribution of degree-­active departments or programs in a given field over time. Share of bachelor’s degrees is a continuous variable operationalized as the percentage of bachelor’s degrees in a given field at a university or college relative to all bachelor’s degrees granted in the same university or college (multiplied by 100 and logged to normalize distribution). Examining this variable allows a better understanding of proportionate growth by identifying the factors affecting local conditions of competition for students in disciplinary and interdisciplinary social sciences. In line with Abbott’s (2001) framework, share is an important measure of organizational competition. In this study, a field with a slipping share of bachelor’s degrees is less able to compete successfully with other fields for students and weakens the demand for faculty trained in that field. Independent Variables

Descriptive statistics for our independent variables are presented in Table 7.2. We investigate the influence of state-­level demographic and economic change on field presence and share of bachelor’s degrees, which Kerr (1991) and others have shown to impact organizational priorities and change among universities and colleges. For this we use data from the U.S. Census Bureau and U.S. Department of Labor Statistics to measure total state population (logged), per capita gross domestic product (logged), and annual unemployment rate. To assess the influence of organizational characteristics and resources on local disciplinary and interdisciplinary change, we also include several organizational-­level variables. These consist of two dichotomous measures of organizational control (1 = private, 0 = public) and highest degree granted (1 = PhD, 0 = less than PhD), and three continuous measures showing percentage of minority students, percentage of female students, and total per capita student expenditures (logged). For organizational control measures, we also calculate organization size as the total

158

disciplines and interdisciplinarity

table 7.2

Descriptive Statistics, 1974–­2004 Min

Max

M

SD

Sociology

0

1

0.63

0.48

Political Science

0

1

0.61

0.49

Geography

0

1

0.18

0.39

Criminology

0

1

0.04

0.18

International relations

0

1

0.15

0.35

Urban studies

0

1

0.07

0.26

Sociology

0.38

8.44

5.20

0.94

Political science

0.96

7.86

5.43

0.89

Geography

0.56

7.31

3.99

0.92

Criminology

0.40

7.68

5.46

1.16

International relations

0.48

9.21

4.67

1.17

Urban studies

0.21

6.15

3.64

1.12

15.68

0.91

Dependent variables: Organizational presence

Dependent variables: Organizational share

Contextual variables

12.74

17.39

State unemployment rateb

2.30

15.60

6.06

1.92

State GDP per capita (Ln)b

9.95

11.68

10.51

0.19

Doctoral

0

1

0.36

0.48

Private

0

1

0.67

0.47

Expenditure per student (Ln)

0

13.97

9.73

0.73

Percentage women

0

100

53.47

16.55

Percentage minorities

0

100

16.67

21.92

Organization size (Ln)

0

6.05

3.22

1.06

Organizational health

0

40

16.28

9.51

State population (Ln)a

Organizational variables

Note: Unless otherwise noted, data are from National Center for Education Statistics, IPEDS database. For years prior to 1985, we used HEGIS obtained from the Inter-­University Consortium for Political and Social Research. a

Data obtained from the U.S. Census Bureau.

Data obtained from the U.S. Bureau of Labor Statistics. State GDP and expenditure per student (full-­time) are adjusted for inflation using the Consumer Price Index and are presented in 2010 dollars.

b

Disciplinary and Interdisciplinary Change

159

number of degree fields (logged), and the mean number of bachelor’s degrees granted per department. In the models specified later, we lag independent variables four years (the standard time frame for completing an undergraduate bachelor’s degree) to account for the delayed effects of organizational inertia. Model Specification

The longitudinal data sets in this study are characterized by unbalanced panel structures. Since repeated observations of the same unit are generally positively correlated, conventional ordinary least squares (OLS) regression methods that assume independence between individual observations are insufficient for analyzing such data. Furthermore, although OLS regression models may produce unbiased coefficients when used with longitudinal data, they tend to produce lower standard errors and p values. Hence coefficients obtained through OLS are not efficient. The most common methods used by social scientists in such situations are fixed effects and random effects regression models (Wooldridge 2002, 247). Among these two approaches, random effects models are preferred for dealing with the data peculiarities in the present study. This is because unlike fixed effects models, random effects models use both within-­and between-­unit variation and can produce estimates for time-­invariant variables.6 Accordingly, we analyze the data with pooled time series random effects regression models.7 For the first set of analyses we use a logistic random effects model since field presence is a dichotomous dependent variable. For the second set of analyses we estimate a generalized least squares (GLS) random effects model because share of bachelor’s degrees is a continuous variable. A random effects model takes the following general form for continuous dependent variables:

yit = μt + βxit + γzi + αi + εit Here yit is the value of the dependent variable for unit i at time point t. While zi is a vector of variables that describe units but do not vary over time points, xit is a vector of variables that vary both over units and over time points. Both β and γ are row vectors of coefficients, and αi “represents all the differences between individuals that are stable over time and not otherwise accounted for by γzi. It can be said to represent unobserved heterogeneity” (Allison 2011, 11). This error structure enables the random effects model to overcome the limitations of conventional regression approaches. Following standard practice, we use robust standard errors in GLS models to ensure that standard errors are not biased by heteroscedasticity. Variance inflation factor scores for all the independent variables were also calculated, with

160

disciplines and interdisciplinarity

none exceeding three. This demonstrates that our estimates were not affected by multicollinearity. Results

Results from a regression analysis of factors predicting presence of fields and their share of bachelor’s degrees at four-­year universities and colleges are summarized in Tables 7.3 and 7.4, respectively. In both tables we report only statistically significant findings and the direction of the effects. (Regression coefficients are available on request.) This strategy simplifies interpretation and facilitates cross-­comparison of the dependent variables across the two groups of disciplines and interdisciplines, which is our main theoretical aim.8 Effects on Field Presence. Our first and most general observation is that interdisciplines appear to be somewhat less sensitive than disciplines to the influence of our selected variables. A simple count reveals four more statistically significant effects among disciplines than among interdisciplines (nineteen and fifteen, respectively). This finding runs against theoretical expectations, given that interdisciplines tend to be smaller and thus less well resourced than traditional disciplines and are considered by many scholars to be more “open”—­by definition—­to outside influences (Frickel 2004). Thus we would expect to find interdisciplines more sensitive to institutional and contextual pressures than disciplines, not less. Our second general observation is that while sibling fields produce statistically significant impacts just seven times in Table 7.3, in all but one instance those results run in the same direction. For example, sociology and criminology are both negatively influenced by increases in per capita expenditures and geography and urban studies are both positively influenced by increases in state population. Taken together these results suggest a structural relationship between sibling disciplines and interdisciplines in ways that generally align with Abbott (2001). More specifically, the results in Table 7.3 do not support the idea that increased budgets positively impact the presence of disciplines and interdisciplines (H1b). Giving mixed support for Hypothesis 1c, we find that private universities and colleges have negative effects on the presence of disciplines, but have positive impacts on the presence of interdisciplines. These effects are highly significant (p < .001) and large. For example, private universities and colleges are 88.5 percent (1-­e-­2.165 = .885) less likely to have political science departments than public universities and colleges. Conversely, the odds that private universities and colleges will have international relations programs are exponentially greater than a public school’s odds for the same outcome. These and other findings suggest that

Disciplinary and Interdisciplinary Change

161

table 7.3 Lagged

Random Effects Logistic Regression Models Predicting Field Presence in Four-­Year Universities and Colleges Sociology

Political science

Geography

State population

+

+

State unemployment rate

–­

Criminology

International relations

Urban studies

+

+

+

–­

–­

State GDP per capita

–­

+

Doctoral

+

+

Private

–­

Expenditures per student

–­

Percentage women

+

–­

–­

+ –­

+

–­

Percentage minority

–­

Organization size

+

+

+

+

+

+

Average degrees per unit

+

+

+

+

+

+

Note: Biannual time dummy variables are not shown.

when private funding fuels the growth of interdisciplinary social sciences, this may happen at the expense of sibling disciplines. Importantly, however, while no other variables in our models produce such starkly contrasting effects, the organizational control variable explains relatively little variation in the dependent variable. Instead, the dominant story related by the specific findings presented in Table 7.3, and prefaced by our more general observations given earlier, is that disciplines and interdisciplines change in ways that are more alike than different. Despite nearly universal assumptions about their distinctive institutional differences—­assumptions that underlie most empirical studies of interdisciplines and interdisciplinarity to date (see Jacobs and Frickel 2009)—­our analysis reveals three variables that show remarkable consistency across the six cases, highlighting the structural similarities between disciplines and interdisciplines. The strongest consistent predictor of presence in our analysis is organization size (H1a), measured as the total number of degree-­active fields at each university and college (logged).9 This variable is responsible for the largest increase in pseudo-­R-­squared, which is a measure of goodness of fit. The coefficient for this variable is positive and highly significant (p < .001) for all

162

disciplines and interdisciplinarity

six cases (analysis not shown).10 The average number of degrees granted per department in each organization is also positive and significant for all six cases. The third highly consistent variable, significant and positive for all but criminology, is state population. The strong support we find for Hypothesis 1a suggests that as predictors of the presence of academic fields, size matters. States with larger populations and universities that have larger numbers of departments and produce proportionately more degrees are all positive predictors of the presence of academic social sciences.11 This finding holds generally for disciplinary and interdisciplinary fields alike and in itself is not surprising: among states and bureaucratic organizations, size typically equates to greater wealth, larger resource pools, and subsequently more influence in shaping administrative decisions. Measured over time, increasing size is a marker of system-­w ide expansion, which is a defining characteristic of the U.S. higher education since the late nineteenth century. What is surprising, given the widespread assumption among scholars, administrators, and policy makers that interdisciplines represent a starkly different way of organizing research and education, is that we find little meaningful difference in the factors shaping their growth compared to disciplines. Effects on Share of Bachelor’s Degrees. A somewhat more complicated picture emerges when we examine the impact of the same set of factors on the share of bachelor’s degrees of fields. These results are summarized in Table 7.4. We again find general differences between sensitivity of disciplines and interdisciplines, although here the differences are considerably more pronounced. Interdisciplinary social sciences are far less sensitive to the influence of independent variables than are disciplines—­as indicated by the comparatively large absence of statistically significant findings on the right side of Table 7.4. With mixed support for Hypothesis 2c, we find that private universities and colleges have negative impacts on the share of two of the three disciplines but a positive impact on just one interdiscipline. Although the effects in this case are weaker, impacting one less field than is reported in Table 7.3, the categorical split in the directionality of those effects between disciplines and interdisciplines remains the same. This finding adds additional support to our earlier interpretation that at private universities and colleges, interdisciplinary growth may be generated at the expense of disciplines—­a finding that runs counter to predictions derived from Abbott’s (2001) theory.

Disciplinary and Interdisciplinary Change table 7.4

163

Lagged Random Effects GLS Regression Models Predicting Share of Bachelor’s Degrees in Four-­Year Universities and Colleges Sociology

Political science

Geography

State population

+

+

–­

State unemployment rate

+

–­

Criminology

International relations

Urban studies

–­

State GDP per capita Doctoral Private

–­

Expenditures per student

+

+

Organization size

–­

–­

Average degrees per unit

–­

–­

+

+

+

Percentage women Percentage minority

–­

+

–­

–­

+ –­

–­

Note: Biannual time dummy variables are not shown.

Three other relationships are markedly different from the analysis of field presence. One difference is concentrated among the three disciplines and involves the predictive role of expenditure per student, which we include in our models as a measure of organizational resource capacity. Whereas in Table 7.3 the effect of per capita expenditures is either insignificant or has a negative influence on field presence, countering our expectations in Hypothesis 1b, in Table 7.4 the same variable has uniformly positive impacts on share. Additional research will be needed to develop an explanation for this finding. Another interesting difference involves the average number of degrees per unit. In Table 7.3 we reported that this measure has a significant and positive impact on field presence. In Table 7.4 this relationship largely drops out, and where the effect remains significant (in sociology) the impact is negative, not positive. One possible explanation is that disciplines simply cost more to administrate than programs, which tend to concentrate in interdisciplines. The third difference we highlight is more important, involving the way that organization size relates to share. As reported in Table 7.3, this variable was

164

disciplines and interdisciplinarity

uniformly related to increasing field presence. In Table 7.4, as predicted in Hypothesis 2a the relationship is reversed. Organization size is a significant predictor of decreasing share for all six fields (p < .001 for all cases). This variable accounts for the largest positive change in R2, explaining the largest portion of the variation in the response variable. This means for example that a 10 percent increase in the total number of degree-­active units at a university yields a 6 percent decrease in the average share of degrees in sociology (1 –­[1.10^b] = 1 –­[1.10^ –­0.669] = 1 –­0.94 = 0.06) and a similar 6.1 percent decrease in the average share of degrees in criminology. Controlling for other factors, larger universities and colleges increase the presence of social science disciplines and interdisciplines, but in ways that shrink the shares of the academic departments and programs that confer those degrees. Organization size impacts field presence positively because once those programs are created it is relatively difficult, barring financial crises, to close them down. It is much easier, however, for administrators to divert resources from those units in ways that reduce those units’ capacity to produce bachelor’s degrees. When this happens, fields can become less competitive without necessarily becoming smaller. Alternatively, administrators might maintain resource levels in existing social science programs but increase resources elsewhere, in effect drawing new undergraduate majors away from the social sciences. Either mechanism, or more likely some combination of the two, may be diminishing the share of social science disciplines and interdisciplines alike, in effect generating larger fields that produce relatively fewer degrees.

Conclusion This study investigates organizational and social factors conditioning the growth of different social science fields by comparing change among three disciplines and three interdisciplines over three decades. While innovative in its methodological design and theoretical aims, our study is not without certain limitations. One limitation is that the focal attention we give to the organizational aspects of undergraduate education leaves aside other important issues, such as graduate education, that may be more instrumental for understanding change dynamics in research-­oriented fields such as international relations. Another limitation is that complete HEGIS/IPEDS data for our fields of interest are unavailable before 1974. And while these data make it possible to map organizational change at the field level, they tell us little about change within disciplinary specialties or about research practices and the content of disciplinary and interdisciplinary knowledge. Finally, these data are specific to the U.S. case, and we caution against extrapolating our findings to other national higher educational systems.

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Against these limitations stand some considerable advantages that our study’s innovative design offers toward deepening understanding of academic change. To our knowledge, this is the first sociological study to develop a comparative analysis of social science disciplines and interdisciplines using longitudinal quantitative data. Also new are our efforts to merge and structurally transform IPEDS and HEGIS data in ways that permit analysis of academic fields across the entire period for which these data exist using the largest possible sample of relevant universities and colleges. And, we use this unique data set in new ways that extend analysis of field-­level changes beyond simple unit counts to also measure whether organizational populations are gaining or losing resource capacities through shares of undergraduate majors. Results show that stability, not increase, has been the dominant trajectory for interdisciplines and disciplines alike. Moreover, we find more historical variation within disciplines and interdisciplines than between them. These findings lend provisional support to those few scholars, such as Andrew Abbott (2001), whose theoretical work emphasizes structural features of the academic system that tie disciplines and interdisciplines closely together. However, it is important not to overstate this claim. The study’s second major finding is that the similarities we find are not uniform; both types of fields change in different ways. Fields can change in size to involve more or fewer academic units, but fields can also become more or less robust, accounting for larger or smaller shares of bachelor’s degrees. Used together, these two measures capture overlapping dimensions of academic change and permit a more variegated picture of such change to emerge. While more research is needed to identify the mechanisms underlying these dynamics, our study draws attention to one key part of the puzzle. This concerns the inverse effects of several variables on field presence and field share, suggesting that as field size increases, the units constituting the field will tend to become less competitive locally. Over time, social science fields may become too big to succeed. This caution applies equally to interdisciplines and disciplines, providing a useful refinement of Abbott’s (2001) theory, which underplays the importance that organizational context can have on shaping system-­level change. It also has important implications for university policies aiming to enhance research and education through interdisciplinary reorganization. As Jacobs (2013) has recently suggested, a more judicious if paradoxical path to interdisciplinary reform may be to reinvest in existing units, which, more often than not, are organized as disciplines. The novel approach and findings developed from this study point to several potentially fruitful avenues for future research. Most obviously, researchers might extend analysis beyond the social sciences, making broader use of information for more than eleven hundred academic fields catalogued in the IPEDS

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database. Rapidly changing sectors of the academy such as the biomedical and health sciences or professional fields such as law, business, and engineering may reveal considerably different dynamics than in the social sciences, where institutional growth over the past three decades has been relatively slow. It may also be useful to combine these longitudinal data with more case-­specific historical data (Mody this volume; Panofsky this volume) and also perhaps bibliometric data on subdisciplinary or transdisciplinary collaborative networks (Light and adams this volume; Moody 2004); doing so will permit a more contextualized account of academic change processes than can be achieved using a single quantitative data source. Whatever forms it takes, future research on interdisciplinarity needs to take disciplinarity into account. Comparative analysis will aid more comprehensive understanding of the forces shaping higher education’s organizational ecology and the knowledge the system produces. It will also sharpen our collective ability to shape research and education policy, in which debates about interdisciplinarity continue to be fueled more by polemic and blind faith than by research. We intend the present study to be a promising step in this direction. notes

This chapter is much improved for the wise and considerate comments we received from Mathieu Albert, Elise Paradis, and Barbara Prainsack. 1   We selected traditional social science fields that have historically related “sibling” interdis-

ciplines for comparison that are recorded in the HEGIS/IPEDS data, a criterion that excludes anthropology and economics. 2   Of these three fields, the latest to develop was urban studies. Although offered in courses in schools of architecture as early as 1933 (e.g., at MIT; Massachusetts Institute of Technology n.d.), urban studies did not gain a broad footing among the social sciences until the early 1960s (Bowen et al. 2010). 3  While international relations has origins in a transnational project of world politics (Schmidt 1998), in the U.S. academy this interdiscipline also promoted a distinctly domestic vision of geopolitics driven by American foreign policy interests and was nurtured by institutional conditions that Hoffman (1977) claims existed only in the United States (see also Smith 2000). 4   We make no claims about the knowledge content or culture practices that may mark these fields as epistemically similar or different. 5   The book was reviewed widely and according to Google Scholar has been cited 1,188 times as of December 3, 2015 (Google Scholar 2015). 6   Fixed effects models are inappropriate in this study because they rely solely on within-­unit variation thus eliminating all between-­unit differences. This is highly problematic for our sample for practical and theoretical reasons. Practically, most variables in our data set contain very little within-­unit variation but do show significant between-­unit differences across years. In this instance, fixed effects models would produce highly erroneous estimates. Theoretically, differences between universities across time are as important as the differences within

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them, in terms of determining the trajectories of disciplines and interdisciplines. And, since fixed effects models already “control for” variables that do not change over time, they cannot produce estimates of theoretically important variables such as organizational control (public vs. private) or whether or not an institution grants PhDs. 7   Presented results are generated from two-­level hierarchical models. We also utilized three-­ level hierarchical models by nesting measurement occasions in our units (higher education institutions), which are further nested within U.S. states. While we obtained highly similar results with these models, we also found them to be much less parsimonious and computationally unstable. 8   To ensure that our results are not a product of the large sample size, we repeated our analyses with smaller random subsamples. These analyses, not shown, produced identical results. 9   This variable is highly correlated with total student enrollment (logged), another measure of organization size that we exclude from the present analysis. In analyses using both variables, the impact of the number of degree active units on campus is so strong that even the addition of a collinear variable (enrollments) does not change the effect. However, using two collinear variables does alter the behavior of other important variables in the models, as expected. 10   Specifically, for each unit increase in the logged number of degree active fields the odds of program presence increases by a factor of e3.607 = 36.855 for sociology, e4.635 = 103.027 for political science, e5.616 = 274.788 for geography, e1.442 = 4.229 for criminology, e2.748 = 15.611 for international relations, and e2.509 = 12.292 for urban studies. 11   To ensure that our state-­level analyses are not biased by potential outliers, we repeated the analyses by adding dummy variables for states with very large populations (California, Texas, New York, Florida) and those with very small populations (Wyoming, North Dakota, and Alaska) in stepwise fashion. We observed no meaningful differences between analyses with or without these dummy variables (analyses not shown). references

Abbott, Andrew. 2001. Chaos of Disciplines. Chicago: University of Chicago Press. Allison, Paul D. 2011. Longitudinal Data Analysis Using Stata. Seminar notes, Boston. Bowen, William M., Ronnie A. Dunn, and David O. Kasdan. 2010. “What Is ‘Urban Studies’? Context, Internal Structure, and Content.” Journal of Urban Affairs 32 (2): 199–­227. Brint, Steven G., Kristopher Proctor, Kerry Mulligan, Matthew B. Rotondi, and Robert A. Hanneman. 2012. “Declining Academic Fields in U.S. Four-­Year Colleges and Universities, 1970–­2006.” Journal of Higher Education 83 (4): 582–­613. Brint, Steven G., Kristopher Proctor, Lori Turk-­Bicakci, and Scott P. Murphy. 2009. “Expanding the Social Frame of Knowledge: Interdisciplinary, Degree-­Granting Fields in American Colleges and Universities, 1975–­2000.” Review of Higher Education 32 (2): 155–­183. Calhoun, Craig, and Diana Rhoten. 2010. “Integrating the Social Sciences: Theoretical Knowledge, Methodological Tools, and Practical Applications.” In The Oxford Handbook of Interdisciplinarity, edited by Robert Frodeman, Julie T. Klein, and Carl Mitcham, 103–­ 117. New York: Oxford University Press. Frank, David John, and Jay Gabler. 2006. Reconstructing the University: Worldwide Shifts in Academia in the 20th Century. Palo Alto, CA: Stanford University Press. Frank, David J., Evan Schofer, and John C. Torres. 1994. “Rethinking History: Change in the University Curriculum, 1910–­90.” Sociology of Education 67 (4): 231–­242. Frickel, Scott. 2004. “Building an Interdiscipline: Collective Action Framing and the Rise of Genetic Toxicology.” Social Problems 51 (2): 269–­287.

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Frodeman, Robert, Julie T. Klein, and Carl Mitcham, eds. 2010. The Oxford Handbook of Interdisciplinarity. New York: Oxford University Press. Geiger, Roger L. 1986. To Advance Knowledge: The Growth of American Research Universities, 1900–­1940. New York: Oxford University Press. ———. 1993. Research and Relevant Knowledge: American Research Universities since World War II. New York: Oxford University Press. Google Scholar. 2015. “Chaos of Disciplines.” https://scholar.google.com/scholar?cites=2377 142654854406262&as_sdt=5,40&sciodt=0,40&hl=en. Hoffman, Stanley. 1977. “An American Social Science: International Relations.” Daedalus 106 (3): 41–­60. Ilhan, Ali O. 2013. “The Growth of the Design Disciplines in the United States, 1984–­2010.” PhD dissertation, Department of Sociology, Washington State University. Jacobs, Jerry A. 2013. In Defense of Disciplines: Interdisciplinarity and Specialization in the Research University. Chicago: University of Chicago Press. Jacobs, Jerry A., and Scott Frickel. 2009. “Interdisciplinarity: A Critical Assessment.” Annual Review of Sociology 35 (1): 43–­65. Kerr, Clark. 1991. The Great Transformation in Higher Education, 1960–­1980. Frontiers in Education. Albany: State University of New York Press. Klein, Julie T. 1990. Interdisciplinarity: History, Theory, and Practice. Detroit: Wayne State University Press. Liefner, Ingo. 2003. “Funding, Resource Allocation, and Performance in Higher Education Systems.” Higher Education 46 (4): 469–­489. Martin, Geoffrey J. 2005. All Possible Worlds: A History of Geographical Ideas. New York: Oxford University Press. Massachusetts Institute of Technology. n.d. “Department of Urban Studies & Planning. About.” http://dusp.mit.edu/department/about. Moody, James. 2004. “The Structure of a Social Science Collaboration Network.” American Sociological Review 69: 213–­238. National Association of College and University Business Officers and Commonfund Institute. 2012. “U.S. and Canadian Institutions Listed by Fiscal Year 2011 Endowment Market Value and Percentage Change in Endowment Market Value from FY 2010 to FY 2011.” http://www.nacubo.org/Documents/research/2011_NCSE_Public_Tables_ Endowment_Market_Values_Final_January_17_2012.pdf. Accessed September 18, 2012. Olzak, Susan, and Nicole Kangas. 2008. “Ethnic, Women’s, and African American Studies Majors in U.S. Institutions of Higher Education.” Sociology of Education 81 (2): 163–­188. Rojas, Fabio. 2006. “Social Movement Tactics, Organizational Change and the Spread of African-­American Studies.” Social Forces 84 (4): 2147–­2166. Ross, Dorothy. 1991. The Origins of American Social Science. Cambridge: Cambridge University Press. Schmidt, Brian C. 1998. The Political Discourse of Anarchy: A Disciplinary History of International Relations. Albany: State University of New York Press. Short, James F., Jr., and Lorine A. Hughes. 2007. “Criminology, Criminologists, and the Sociological Enterprise.” In Sociology in America: A History, edited by Craig Calhoun, 605–­638. Chicago: University of Chicago Press. Smith, Steve. 2000. “The Discipline of International Relations: Still an American Social Science?” British Journal of Politics and International Relations 2 (3): 374–­402. Toescu, Emil C. 2005. “Integration and Interdisciplinarity in Contemporary Neurosciences.” Journal of Cellular and Molecular Medicine 9 (3): 529–­530.

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Turk-­Bicakci, Lori A. 2007. “The Development of Social Movement Programs and Departments in Higher Education: Women’s and Ethnic Studies from 1975 to 2000.” PhD dissertation, University of California, Riverside. Turner, Stephen. 2000. “What Are Disciplines? And How Is Interdisciplinarity Different?” In Practising Interdisciplinarity, edited by Nico Stehr and Peter Weingart, 46–­65. Toronto: University of Toronto Press. Weerts, David J., and Justin M. Ronca. 2006. “Examining Differences in State Support for Higher Education: A Comparative Study of State Appropriations for Research I Universities.” Journal of Higher Education 77 (6): 935–­967. Wooldridge, Jeffrey M. 2002. Econometric Analysis of Cross Section and Panel Data. Cambridge, MA: MIT Press.

8 ◆ “An Elec tro-­Historical Focus with Real Interdisciplinary Appeal” Interdisciplinarity at Vietnam-­Era Stanford C y r u s C .   M . M o dy

American academic scientists faced many challenges circa 1970: declines in funding, protests over research, declining deference to scientific authority. Responses were diverse. Some, such as openness to parapsychology, were short-­lived (Kaiser 2011). Others, such as “divestment” of institutes conducting classified research, were enduring but cosmetic (Leslie 2010). Yet others, especially increasing academic entrepreneurship, have outlived the crisis conditions in which they emerged (Mowery et al. 2004). This chapter examines interdisciplinarity as perhaps the only response to conditions around 1970 that universities’ conflicting constituencies could agree on, though each for different reasons. The interdisciplinarity vogue of the late sixties and early seventies is enlightening for several reasons. As I will show by quoting from scientists, activists, and academic administrators, some of the rhetoric of that era is parroted in today’s calls for greater interdisciplinarity. Yet the interdisciplinarity of that era was intended to solve a different, and perhaps more diverse, set of problems. Much of the pressure for interdisciplinarity then came from countercultural institutions and groups—­a fact rarely acknowledged today. Many avowedly interdisciplinary research organizations founded in the early seventies still exist, offering an almost half-­century-­old natural experiment in how, and how well, such organizations enact interdisciplinarity on the ground. Perhaps most importantly, 173

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optimism about and hunger for interdisciplinarity receded by the mid-­seventies, both because difficulties arose in answering vague but urgent calls for interdisciplinarity, and because the political and cultural conditions that gave rise to those calls disappeared. The travails of early seventies interdisciplinary could be taken to undermine the inevitable and self-­evident character of much of today’s discourse about interdisciplinarity.

Stanford To unpack interdisciplinarity circa 1970, this chapter offers a case study of Stanford University. Stanford’s leadership in building the military-­industrial-­ academic complex in the early Cold War is well documented (Lowen 1997; Leslie 1993; Leslie and Kargon 1996; Lécuyer 2006; O’Mara 2005; Sturgeon 2000). So is its emergence in the 1980s as a model entrepreneurial university, closely associated with Silicon Valley and the San Francisco biotechnology cluster (Colyvas 2007; Nelson 2005; Smith Hughes 2001; Yi 2008; Lenoir and Giannella 2006; Kenney 1986). Much less is known about Stanford’s rocky transition between the early Cold War and the Reagan era, however (Vettel 2006). Those years were turbulent largely because Stanford’s earlier success obtaining grants from the military and national-­security-­oriented agencies such as NASA and the Atomic Energy Commission left it open to antiwar criticism and budgetary shortfalls in military research funding during the Vietnam era. In 1969–­1970 alone, protestors occupied Stanford’s Applied Electronics Laboratory for a week, the Stanford Research Institute was divested, classified research was banned from campus (with a consequent two-­million-­dollar budget shortfall), and President Kenneth Pitzer was forced out. Though protests quieted after 1971, strained funding left labs scrambling for another decade. Throughout, interdisciplinarity was a ubiquitous tool in Stanford’s response to campus protest and funding shortfall. Before the late sixties, the interdisciplinary interactions Stanford administrators fostered—­for example, among electrical engineers, metallurgists, and applied physicists—­were bare compared to those in the Vietnam era, when the same departments sought partners in music, medicine, philosophy, economics, and so on (Leslie 1987). In the fifties, Stanford’s legendary postwar provost, Fred Terman, saw interdisciplinarity largely in terms of reorganizing the life, social, and engineering sciences around tools borrowed from physics. For instance, he pressured Electrical Engineering to abandon power transmission research and take up physics-­based fields such as microwave communications, and he remolded old-­ fashioned “metallurgy” into “materials science” (the Stanford Metallurgy Department was second in the country to change to “Materials Science”). Terman also pushed Political Science to hire behavioralist faculty familiar with quantitative techniques from physics (Lowen 1997, 213). In Biology, he

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sidelined older faculty who were not reductionist, quantitative, and successful in winning federal grants—­characteristics perceived as fundamental to physicists’ success. Terman reorganized the Medical School along similar lines by appointing a dean who believed that “medical progress now comes not from the bedside but from the laboratory . . . [and] the basic sciences,” and that “the medical faculty’s prime reason for existence is research, not practice” (Vettel 2006, 58). Thus, in the early Cold War—­up to about 1966—­Terman and other Stanford administrators viewed interdisciplinarity as a means for promoting partnerships with national security agencies and high-­tech defense industries and for enhancing the school’s profile in basic research by reorganizing applied, holistic fields around more prestigious abstract, reductionist topics. By the late 1960s, though, Terman’s model of interdisciplinarity faltered because the national security funding it depended on began to dry up, and because Americans began to clamor for the kinds of holistic, applied research that Terman’s model minimized.

Interdisciplinarity and Problem-­Oriented Research I’ll tackle the latter, cultural shift first, because in many ways it caused, and was thornier than, the decline in national security research funding. Americans of all persuasions saw their country as facing profound but tractable problems in the late 1960s. Many—­at both the grassroots and the governing elite—­thought that academic scientists could help solve those problems if they made their research more (in the language of the time) “relevant.” This widespread view is nicely captured in a list compiled by Rudi Kompfner (~1971a), a Bell Labs engineer who retired into a Stanford professorship: What are the most urgent problems facing the nation today . . . ?

•  •  •  •  •  •  •  • 

Decay of the big cities Mass transportation Over-­population Pollution of the environment Availability of health care Natural resources and energy planning Un-­ and under-­employment Integration of races, etc.

Once Congress has expressed its wishes in these matters, it is possible to assess sciences with respect to the contributions they may be expected to make in the various problem areas identified above.

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Those who promoted such topics invariably argued they were too complex for mono-­disciplinary investigation. As Stephen Kline, a mechanical engineer who cofounded Stanford’s Values, Technology, and Society program, put it, “The kinds of questions that do and should concern the students are: Do you build the SST [supersonic transport], and what is being done about smog? Questions of this sort cannot be seen clearly through the viewpoint of any single discipline” (quoted in Campus Report 1971d). Even administrators skeptical of civilian “relevance” emphasized the inherent interdisciplinarity of the public’s favored problems. For instance, a 1970 campus newspaper offered the administration’s view: dr. miller: society should “strike balance” in research activities “Universities should and are moving from the isolation of their ivory towers, yet they should not and cannot become a Red Cross or service station for every social problem,” says William F. Miller, Stanford University’s Vice Provost for Research. In the past, he believes, there was an overemphasis on discipline-­oriented research; “just as there is probably the beginning of a slight overreaction now” with funds going to more problem-­oriented research. (Campus Report 1970b)

Problem-­oriented research’s inherent interdisciplinarity was implied by widespread beliefs about the interdisciplinary structure of applied, national-­security-­ oriented projects. Sponsors of high Cold War projects themselves emphasized that they required greater interdisciplinary collaboration than could be found in universities. For instance, administrators at NASA—­nominally civilian but closely linked to the military—­informed Stanford in 1968 that they disliked “engineering doctoral programs . . . modeled after the more classical ones for scientists with research and studies directed at highly specialized work in physics rather than toward advanced engineering problems,” and instead preferred engineers educated to “deal with economic, social, legal, business, and management issues” (Stanford-­NASA Memorandum of Understanding 1968). Protestors understood NASA’s point and embraced the interdisciplinary methods of national security R&D—­but only because they rejected the ends to which those methods led. In that vein, one of the most instructive and explosive episodes at Stanford concerned the Stanford Research Institute (SRI), a contract research outfit founded in the late forties. By the late sixties SRI was engulfed in controversy over research on chemical and biological warfare and counterinsurgency-­inspired “land reform” in Vietnam. Protests, culminating in 1969, successfully used SRI to implicate Stanford in the Southeast Asian conflict. Two ways forward were proposed: to “divest” SRI, or to turn it into Stanford’s clearinghouse for applied, interdisciplinary, civilian research. Proponents

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of the latter noted that SRI’s interdisciplinary methods were invaluable to the military—­so why shouldn’t they benefit civil society? As the occupiers of the Applied Electronics Laboratory put it, A program of research to benefit all peoples will necessarily transcend narrow disciplinary perspectives and consider the interrelation of many aspects of each research problem. The physical impact and psychological implications of technological progress must be continually projected. Stanford Research Institute has already developed an interdisciplinary approach to many of its projects. Our objection to the present content of SRI research does not keep us from appreciating the need for this approach. (“A Message from the Stanford Sit-­In” 1969)

Unsurprisingly, divestment won out, as at MIT and other schools with similar institutes. Protestors offered no plan for keeping SRI solvent, and they angered the faculty by insisting SRI pass a social acceptability test. Critics successfully argued that protestors threatened academic freedom and research quality because their plans would force scientists to follow the whims of the public, rather than the meritocratic guidance of disciplinary peers.

Other Motivations The allegedly inherent interdisciplinarity of applied research still legitimizes interdisciplinarity on American campuses. Yet circa 1970 many other justifications for interdisciplinary collaboration were offered that seem alien or irrelevant today. Chief among these was the hope that interdisciplinarity would prevent universities from falling apart. Although fears that institutions such as Stanford would go extinct may seem overblown today, the deep racial, political, and generational divides of the time led many to believe that universities had become ungovernable. Greater cross-­disciplinary interaction seemed one solution to such existential crisis. As President Richard Lyman said in 1970, We ought to glory in the fact that some people are learning to appreciate Keats in one part of the campus, while others are solving problems of linear programming in another. Glory in it, and make a towering virtue of necessity by exposing the one group to the other, and each to a thousand further groups, at every available opportunity. It is an arrogant assumption of some humanists that no computer man reads Keats, and no electronics buff can dig Scarlatti. It is an arrogant counter-­ assumption of some technologists that no humanist has anything important to contribute to life in the technitronic age of the future. (quoted in Campus Report 1970a)

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Surprisingly, perhaps, Students for a Democratic Society (SDS 1968a, emphasis original) inaugurated Lyman’s predecessor’s brief, violence-­riddled tenure as president with similar rhetoric: Welcome Pitzer!! On our campus, as in other communities, we are isolated from each other. We have no real community. And we, as a group, are isolated from people outside the Stanford Community. The research on [sic] our university is not in the interests of most people. Much research is basic research with no intrinsic value of its own! Other work supports the war or government policies which cause wars. Not enought [sic] scientific resources are dedicated to the channeling of Nature’s resources and the disposal of Man’s wastes (smog, pollution, etc.).

Elsewhere, the Stanford SDS (1968b) argued that “college students are trained to fit into vocational niches already defined for them by government, industry, and universities. The courses offered merely represent ‘choices’ in a limited number of narrowly defined fields. . . . Each department has its own hardened arteries. . . . SDS people believe that students should be offered the opportunity to freely reach into other fields, and ‘broaden their minds.’ ” The SDS critique resembled the university’s own Study of Education at Stanford, which concluded in 1968 that “areas that call for early attention in new course development include: science and technology for the nonspecialist; mathematics and computer science for the nonspecialist; interdisciplinary and problem-­oriented studies in the humanities and the social sciences” (Campus Report 1968b). Possibly some on the SES committee sympathized with the New Left critique, but most likely the committee as a whole advocated an interdisciplinary curriculum for institutionally conservative reasons such as placing alumni in a worsening job market. Indeed, administrators’ rhetoric was grittily pragmatic in promoting humanistic, interdisciplinary fields. According to the Campus Report in 1970, cutbacks in federal and state budgets, the general condition of the economy, showed that we were heading into a period of serious financial constraint.  .  .  . Among the tough-­minded decisions that have been made, the Vice Provost said, is to lay greater stress on interdisciplinary programs for undergraduates, such as human biology, environmental engineering, and “values, technology, and society.”  .  .  . The University will stress a thoroughly trained undergraduate body which can be expected to go into graduate professional training in order to attack today’s pressing problems. (Campus Report 1970c)

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Other “tough-­minded” reasons for fostering interdisciplinarity included “giving young faculty members ‘a sense of educational power’ through developing and evaluating new programs which cut across normal departmental lines [which] can be ‘the key factor in cultivating loyalty to the institution as a whole’ according to Prof. [Vice Provost Herbert L.] Packer” (Campus Report 1968a). Stanford administrators also saw financial benefits in interdisciplinarity. For example, the justification offered for a new Center for Interdisciplinary Research in 1972 was that “the Stanford faculty has demonstrated a substantial and growing interest in the development of interdisciplinary research. Much of this is oriented toward what might broadly be called ‘problems of society.’ . . . The increasing availability of external support insures that the faculty will be able to translate these interests into effective research activities” (Massy 1972). Some administrators also sensed that interdepartmental cooperation signaled to funders that the university was not disintegrating. As President Lyman (2009, 161–­162) recalled, The Ford Foundation’s grant of nearly $2 million enabled the university to launch the most successful of interdisciplinary undergraduate majors, human biology. As I recall, what influenced the foundation . . . was the spectacle of a founding committee that included several chairs of Medical School clinical departments, not a group generally to be found putting time and energy into developing innovations in undergraduate education. Human biology increased Stanford’s reputation for entrepreneurial activity in academic matters, perhaps more than any other development of that period, and showed that as an academic institution we were refusing to be stymied by the campus unrest.

Thus, institutional motivations for promoting a wider-­ranging interdisciplinarity circa 1970 were extremely heterogeneous. Individual attitudes toward interdisciplinarity were quite mixed as well. A striking example of individual mixed motives is Rudi Kompfner, the electrical engineer quoted earlier. Kompfner’s correspondence from the early seventies overflowed with several distinct, apparently equally heartfelt, encomiums to interdisciplinarity depending on his audience. Sometimes he framed interdisciplinarity as satisfying Congress’s demands for “relevant” research. Sometimes an interdisciplinary outlook was key to finding biomedical users for technologies he had invented (Kompfner 1976). Sometimes this son of a Viennese composer lapsed into earnest speculation about the commingling of music, technology, and human mentation (Kompfner ~1971b). None of these motivations were mutually exclusive, and probably none exclusively represented Kompfner’s “real” motivation. Rather, Kompfner’s

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complicated individual yen for interdisciplinarity indicates how messy aspirations for interdisciplinarity were across the entire university.

New Visibility Stanford enacted these multiple visions for expansive interdisciplinarity largely in three ways: new visibility for extant interdisciplinary projects; new interdisciplinary projects modeled on the older, newly visible ones; and new interdisciplinary institutions. Perhaps the most striking example of the first was the Optacon (or OPtical to TActile CONverter). Rudimentary attempts at this tactile reading aid for the blind dated to 1963, well before budget crises and student protest. John Linvill, chair of Electrical Engineering, led the Stanford side of the project, with another electrical engineer, James Bliss, joining from SRI. Linvill’s inspiration, in part, was personal; his daughter, Candace, was blind and eventually became the Optacon’s most prominent user. However, both Bliss and Linvill started with national security funding from the Office of Naval Research, NASA, and the Air Force (Linvill 1973). Even well before student protest erupted, Linvill (1967) was already outlining how interdisciplinarity would provide a bridge away from national security funders: Stanford University can and should become more effective in studying and attacking the problems of today’s society. Electrical Engineering, with its aim to bring technological tools to the solution of man’s problems, is interested to join with other departments in working on these contemporary problems. . . . These problems cannot be attacked within a single discipline. Technology, by itself, is not sufficient. Multidisciplinary approaches must be used. . . . Financial support for the research must be found outside the university, however, as has been the case in our DOD and space research.

Later that year, Linvill and Bliss submitted a proposal to the federal Office of Education for equipment for fabricating integrated circuits to make the Optacon fast enough to compete with Braille. That equipment was housed in the Integrated Circuits Laboratory run by James Meindl, a new recruit from the Army Signal Corps. Meindl’s involvement sped Optacon development, and “by April 1969 three improved reading aids . . . were being used in reading experiments with blind students” (Linvill 1973, 10). April 1969 was also the peak month of research-­oriented protest at Stanford. In response, administrators moved to show that Stanford was already doing interdisciplinary, civilian, problem-­oriented research. Tellingly, they put the Optacon on a pedestal in outlets such as the university’s annual Electronics

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Research Review. In reviews prior to 1969, the reading aid received terse descriptions buried at the back. But in 1969, the Optacon was given a gushing ode on page one, emphasizing that “integrated circuits, principally developed to the present stage for space and military applications, are powerful tools for the solution of human problems, channeling interest of the imaginative graduate student to important social problems and providing a vehicle for interesting technical research” (Stanford University Electronics Research Review 1969, 1). Likewise, when a faculty committee organized a 1971 research “exposition extending over the entire campus community. Interdisciplinary activities particularly are desired” (Campus Report 1970d), the exposition highlighted “the use of holography (three-­dimensional laser holography) in displaying art objects, a continuous interactive opinion poll with a computer terminal, the use of electronic devices to help the blind and to measure medical data inside the body, and a presentation of computer-­composed music” (Campus Report 1971b). That is, the Optacon was repeatedly and prominently used to advertise Stanford researchers’ ability to cross disciplinary lines, address civilian problems, and reach out to local communities. By the early seventies, the Optacon was being field-­tested in local schools and faculty members and graduate students in Stanford’s School of Education and departments of Mechanical Engineering and Psychology (as well as a neuroscientist at the Smith-­Kettlewell Institute) were working with the device. Linvill, Bliss, and Meindl sought funding from the NIH, the NSF’s Research Applied to National Needs program, and the military’s Joint Services Electronics Program in addition to the Office of Education, and even founded a start-­up, Telesensory Systems, to manufacture Optacons. Meindl, meanwhile, used his Integrated Circuit Laboratory to build biomedical devices (e.g., implantable ultrasonic imaging systems) that led to large-­scale NIH funding and collaborations with Medical School researchers. Such projects also defended against antiwar protest. For instance, a student-­led 1971 survey of military-­funded research at Stanford cited Meindl’s Army grant for “Micropower Integrated Circuits,” along with other defense funding composing about 12 percent of Meindl’s research budget. Yet Meindl blunted criticism of such work by arguing that “the only application to date of the results of the Micropower Integrated Circuits contract has been accomplished at Stanford under NIH sponsorship for research on integrated circuits for medical applications” (DOD Sponsored Research at Stanford 1971, 198, emphasis original).

New Research Directions The Optacon’s visibility made it a model for Stanford faculty members adapting to the new interdisciplinary climate, such as Rudi Kompfner and his collaborator, Calvin Quate. Quate was one of the first PhD graduates, in 1950, of Terman’s

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redesigned Cold War university, after which he worked with Kompfner at Bell Labs for almost a decade. After returning to Stanford’s Microwave Laboratory (and joining the departments of Applied Physics and Electrical Engineering as a faculty member) in 1961, Quate became increasingly interested in the coupling of electromagnetic and acoustic radiation in crystals. That work, supported by the Navy, was oriented to signals processing in military radar and communications, but was also (eventually) incorporated into civilian cell phones, garage door openers, televisions, and other gadgets. In 1966, while “sitting around the swimming pool” (National Science Foundation 1978) Kompfner hypothesized that Quate’s devices could form a microscope in which a circuit pulsed a thin film transducer to launch acoustic radiation through a sapphire lens and onto a sample. The acoustic waves would be absorbed or reflected by the sample, detected by a transducer, and converted into an image. Slowly, Quate followed Kompfner’s suggestion, using money diverted from a Navy electronics grant. However, even in this early, military-­ funded phase he and Kompfner framed the microscope in terms of interdisciplinary collaborations and civilian applications. As Kompfner later remarked, “the original intention behind the Stanford work was to make an instrument . . . [for] studying biological objects” (Kompfner 1975, 675). That limitation to biological samples is curious, since earlier proposals for an acoustic microscope (which Quate and Kompfner knew about) were oriented to characterization of defense-­relevant crystalline materials—­an application they deemphasized during the protest years when interdisciplinarity was most in vogue. Instead, Quate sought interdisciplinary collaborators by pushing for Electrical Engineering to make a joint appointment with the Medical School. Linvill (1966), however, warned Quate “that the Biophysics Lab has not succeeded in their efforts to form a joint program with the School of Medicine; and, furthermore, the Administration is acutely aware of this failure at the present moment. Is the School of Medicine the only alternative? I would think a connection with the Biology Department could be just as rewarding and allow for the same sort of interacting research programs.” Nevertheless, Quate began talking with Medical School researchers, one of whom suggested the John A. Hartford Foundation as a funder to wean acoustic microscopy from Navy support. Thus, in 1969, Quate obtained a two-­year, $170,000 grant from the Hartford Foundation, followed by a three-­year, $300,000 grant stipulating that Quate work more closely with biomedical researchers. Over the next few years, Quate imaged samples prepared in the Stanford departments of Surgery, Pathology, Psychology, Radiation Oncology, Hematology, Anatomy, Medical Microbiology, and Biology, as well as from biomedical researchers at the Mayo Clinic, UC Irvine, Albert Einstein School of Medicine, University of North Dakota,

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National Cancer Institute, U.S. Department of Agriculture, and the Faculty of Medicine in Montpellier, France. Yet when Quate tried to leverage those samples into NIH grants, he was told he had not achieved the right degree or kind of interdisciplinary collaboration. Even G. C. Coburn (1976), Hartford’s program director, chided Quate: Although you have had some collaboration from medical scientists in various fields, it has been scattered, transient and too inconclusive to be of value in obtaining NIH support. . . . The trouble with NIH lies in your understandable lack of experience in devising a sound and workable research proposal in the alien field of biomedical research. This is an art in itself and must surely require the active collaboration, dedication and commitment of a leading medical research scientist.

Quate received similar bad news from American Optical, a company that licensed Stanford’s acoustic microscope patents in hopes of selling the instrument to clinicians and biomedical researchers. By 1977 those hopes were dashed, as “real interest in the acoustic microscope as a research tool is withering on the vine” (Berkenstock 1977). Thus, starting around 1974, Quate began seeking alternative application areas and modes of collaboration. Key to this shift was the “discovery” that the acoustic microscope could characterize nonbiological materials. That discovery, in turn, coevolved with shifts in the funding environment for academic engineering science in the wake of the 1973 oil embargo. The Nixon administration expanded funding for energy research, to which Quate and another Stanford applied physicist, Ted Geballe, responded with an NSF proposal to study superconducting power transmission lines. Later, Quate turned acoustic microscopy in this direction as well by applying it to determination of carbon content in coal formations (Quate et al. 1979). More important for Quate, though, was growing national anxiety, especially after 1975, about economic competition from Japan, particularly in microelectronics manufacturing. Here, Quate could form collaborations and find users for his microscope without encountering the disciplinary obstacles he faced working with biomedical researchers. Thus, he quickly developed a new application for acoustic microscopy: quality control inspection of integrated circuits. Quate easily acquired sample integrated circuits, and sometimes funding, from nearby Hewlett-­Packard, Avantek, IBM, and Fairchild Semiconductor. He also noted to federal funders that microelectronics researchers—­unlike life scientists—­ were eager to build their own acoustic microscopes and/or buy them when they became commercially available.

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Microelectronics was also an area where civilian and military interests converged. By the mid-­seventies, campus protests disappeared, as did stigmatization of military funding. In 1974, Quate moved back under the Navy’s umbrella by claiming acoustic microscopy could detect microcracks in military equipment (e.g., jet turbine blades or nuclear reactor shielding). Still, money was tight, and Quate prioritized applications that could garner funding from multiple sources—­such as microelectronics. The military wanted integrated circuits made by American firms and possessing extreme capabilities (e.g., radiation hardening or very high speed for cryptography). Civilian agencies, meanwhile, were also looking to shore up the domestic microelectronics industry. Thus, when the National Bureau of Standards and ARPA teamed up to fund “Innovative Measurement Technology for the Semiconductor Industry” in 1976, Quate obtained a grant to develop acoustic microscopy for microelectronics characterization. The arc of acoustic microscope research stands for the larger arc of interdisciplinary research after 1970. Quate’s acoustic microscopy work began with great hopes of collaborating with biomedical researchers to solve “human problems.” As policy makers’ favored problem areas shifted during the seventies, and as interdisciplinary collaboration proved harder than expected, Quate’s research shifted as well. His ties to life science waned, though into the 1980s he still had students imaging, for example, bacterial organelles. That later work wasn’t, however, presented as a prelude to clinical use, since Quate’s post-­1980 acoustic microscopes operated in liquid helium (a notoriously difficult and expensive fluid that distorts biological tissues from their living forms). Rather, the tiny features inside cells were useful for calibrating the microscope as his students improved its resolution. Nevertheless, the Vietnam era left a mark on Quate. He gained a taste for patenting and licensing his work. He learned to secure funding from philanthropies, corporations, and civilian agencies in addition to the military services. And he acquired a cautious yen for interdisciplinary collaboration, particularly with the life sciences. Thus, when Quate (1983) switched his lab to work on a new tool, scanning tunneling microscopy, in the eighties, he wrote enthusiastically in his notebook that it made it possible that “someday we should return to cells.”

New Institutions The third enactment of the new interdisciplinarity at Stanford was the creation of institutions to unite disciplines and connect them with resources and audiences. As Figure 8.1 (by Andrew Nelson) shows, the late sixties saw a boom in interdisciplinary centers at Stanford. Many of these programs were anchored in the social sciences since, as Jamie Cohen-­Cole (2003) has shown, an ideology of interdisciplinarity already gripped American social science in the fifties. Indeed,

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Number of programs

35 30 25 20 15 10 5

1948 1950 1952 1954 1956 1958 1960 1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002

0

Figure 8.1. Number of degree-­granting interdisciplinary programs at Stanford, 1947–­ 2001. Source: Andrew J. Nelson, “Cacophony or Harmony? Multivocal Logics and Technology Licensing by the Stanford University Department of Music,” Industrial and Corporate Change 14 (2005): 93–­118, by permission of Oxford University Press.

when the university founded a Center for Interdisciplinary Research in 1972, it used an existing interdisciplinary social science center, the Institute for Public Policy Analysis, to nucleate a broadening of “the scope of interdisciplinary research activities to include engineering, the physical sciences, the professional schools and humanities, as well as economics and the social sciences” (Donham 1972). Yet some of Stanford’s most successful new interdisciplinary centers focused on humanistic application of and reflection upon engineering expertise. For instance, mechanical engineering professor Stephen Kline (quoted in Campus Report 1971d) assembled a coalition of “scientists, engineers, philosophers, historians, anthropologists, psychologists, psychiatrists, sociologists, ethicists, and theologians—­all working very closely together” in a new Values, Technology, and Society program that continues today under the banner of “Science, Technology, and Society.” Although Kline’s relationship with campus activists was often tense, reform-­minded faculty members “counterpoise[d] the brilliantly-­ conceived program in Values, Technology and Society to which Kline and [Walter] Vincenti are devoting so much creative energy . . . against the unregenerateness and glacial metamorphosis” of other colleagues (Ashley 1971). New coalitions of researchers and funders also emerged in the late sixties to broaden the interdisciplinarity of existing centers and turn them toward more civilian-­oriented problem areas. As with individual researchers, the decline in funding encouraged centers to broaden their funding streams, and hence their disciplinary base. One such was the Stanford Artificial Intelligence Laboratory

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(SAIL), founded in 1963 as the Stanford Artificial Intelligence Project. By 1968, SAIL had more than a hundred participants, funded by NASA, the NSF, and the National Institute of Mental Health, though the Pentagon, through ARPA, contributed half to two-­thirds of annual funding (Buchanan 1983). That avant-­garde interagency-­interdisciplinary attitude, plus SAIL’s ownership of significant computing power, led in turn to one of Stanford’s most profitable and unusual interdisciplinary collaborations of the Vietnam era (Mody and Nelson 2013). In 1964, an undergraduate SAIL programmer, David Poole, was playing the tuba in the Stanford orchestra near John Chowning, a percussionist and graduate student in the Music Department. Poole heard that Chowning was interested in computer-­ generated music and suggested Chowning use SAIL’s computers. Les Earnest, SAIL’s deputy director, was supportive, and Chowning became a fixture in the lab. Over the next decade, Chowning acquired enough international stature, funding, and local supporters to spin off from SAIL and found the Center for Computer Research in Music and Acoustics (CCRMA—­pronounced “karma,” naturally). From the beginning, CCRMA drew favorable publicity for Stanford in venues such as Newsweek and Rolling Stone. Notably, every popular article about CCRMA in the seventies—­and there were dozens—­mentioned the word “interdisciplinary.” Most articles dwelt on how wide-­ranging the center’s collaborations were. That interdisciplinarity, in turn, allowed CCRMA researchers to satisfy the often conflicting demands of the 1970s. As Chowning and colleagues put it in a 1974 joint proposal to the NSF and the National Endowment for the Arts, CCRMA’s “rich interdisciplinary environment” would allow musicians to “produce fundamental knowledge” for the “science of music,” while at the same time “creative minds from diverse disciplines” would cooperate to “produce works of art which reflect the scientific-­technological riches of the present” (Chowning et al. 1974). Unusually for a music center, CCRMA also approached industry. Indeed, contacts with firms were one thing engineering faculty members could bring to CCRMA in return for the opportunity to work on something new and expressive. For instance, in 1974 Jim Angell, a professor in Electrical Engineering, received a Research Development Fund (RDF) grant for work on “Digital Systems for the Generation of Musical Sound.” The RDF was a pot of money provided by the NSF (matched by Stanford’s own funds), which the university used “to stimulate innovative research endeavors by both junior faculty and faculty who were redirecting their research interests” (Miller 1975). Angell used his RDF money to support Dick Moore, a graduate student in electrical engineering and research assistant at CCRMA who later joined the Music faculty at UC San Diego and directed UCSD’s equivalent of CCRMA. Together, Angell and Moore submitted a proposal “to Intel Corporation, who

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have told us informally that they would be happy to provide us with any amount of . . . integrated circuits” (Angell 1976). Chowning, meanwhile, had started patenting ideas for electronically generating sounds in the mid-­sixties, eventually striking gold with his invention of FM synthesis in 1967. That patent was licensed by Yamaha for their line of electronic instruments and became one of Stanford’s most profitable patents.

Skepticism and Disappointment Yet despite the fame and later fortune he brought the school, Chowning, who had taken an assistant professorship at Stanford after receiving his PhD, was initially denied tenure. Both published and unpublished accounts of his tenure case point to a classic problem of interdisciplinarity: musicians couldn’t judge his electronics, and electrical engineers couldn’t judge his music (Lehrman 2005). Stanford reversed its decision only when Yamaha sought Chowning’s help implementing patents they licensed from Stanford, and when other schools offered Chowning tenured posts. Chowning’s tenure case and Calvin Quate’s difficulties with the NIH highlight one obstacle to expansive interdisciplinarity in the 1970s—­not everyone liked it, or thought that it would help Stanford through crisis. Even administrators who were publicly well-­disposed toward interdisciplinarity often sounded pessimistic notes. President Lyman (quoted in Campus Report 1971a), for instance, cautioned that the trick will be to assure effective linkages between so-­called traditional disciplines and skills, on the one hand, and problem-­oriented or experiential education on the other. Without such linkages, the contemporary American university could descend  .  .  . into the biggest and most over-­financed federation of bull sessions in history. . . . Research freedom will diminish in future, if only because complex inter-­disciplinary research teams cannot operate with the full flexibility of a project originated and run by a single Principal Investigator.

Elsewhere, Lyman (quoted in Campus Report 1971c) acknowledged that “no matter how earnestly the effort is made by scholars to mount collaborative attacks on social problems, he said, ‘results are bound to be slow and halting. . . . It will take more than some marriages among academic disciplines to cope with the present widespread distrust of all attempts at objectivity.’ ” Others, such as Dean of Engineering William Rambo, were more scornful, especially in private. A satirical speech by Dean Rambo (1970) at a dinner marking the end of classified research at Stanford offers a striking look at the backlash against interdisciplinarity:

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As part of our program to update the undergraduate curriculum, I can report two new courses. The Freshman Seminar on “The Luddites and Creativity in Engineering” has met with only modest success, this despite an electro-­ historical focus with real interdisciplinary appeal. We have done much better, measured by attendance figures, with the senior-­level offering entitled “Pornography in Science.” Frankly, we see this course combination as a frontal attack on a two-­sided problem, as a sort of intellectual bridge uniting C.P. Snow’s two cultures. But this is not the end of our innovation binge! We are readying an exciting new course for Spring Quarter, as yet untitled. It follows suggestions of consultants from the other Schools . . . and will make full use of advanced encounter techniques evolving naturally from Dramatic Play, Creative Playtime, Show & Tell, and Sesame Street. . . . Our program meets completely this week’s Research Guidelines, and . . . in accord with Community urging, we hope (and by next month’s deadline) to have solved the assigned research problems clearing difficulties with transportation, low-­cost housing, health care, the Post Office, pollution, ecology, Mrs. Mitchell, and population control. We are particularly mindful of the last topic, some local students having established well, if indirectly, the case for population control about 20 years ago.

For Rambo, radical interdisciplinarity was the symptom and source, not the solution, for Stanford’s ills. Interdisciplinarity, he believed, had been imposed on the School of Engineering by “the other Schools” in a fashionable, unrealistic, hedonistic, and self-­absorbed attempt to solve society’s problems all at once. Rambo was right—­on some counts. Radical interdisciplinarity proved difficult. Funding trends of the seventies were, indeed, unstable, faddish, and poorly organized. Pressure to justify research purely by its application is still difficult to reconcile with academic freedom and the university’s critical function—­though surely that would be true whether the source of pressure were the Pentagon, the SDS, or the neoliberal market. Yet skeptics’ pessimism was also overblown. The end of classified research did not cripple Stanford; “electro-­historical” ventures such as the Values, Technology, and Society program have not (I hope!) proven worthless; and some “fashionable” research areas such as alternative energy have stubbornly endured. Even Rambo’s diagnosis that pressure for interdisciplinarity and civilian application was imposed only from outside was incorrect, since some Stanford scientists’ and engineers’ own qualms about defense work motivated them to seek to contribute more directly to solving civilian problems.

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From Active to Passive Interdisciplinarity . . . and Back Again Moreover, even when radical interdisciplinary ran aground in the short term, it reoriented the outlook of those who attempted it in ways that resurfaced later. Still, there was a period of retrenchment after 1973. Protests waned, providing less leverage to force researchers toward “human problems.” Agencies that had earlier turned toward applied human problems, especially the NSF, turned back under pressure from Congress, the scientific community, and their own grant officers (Belanger 1998). Above all, the economy remained stagnant, so agencies looked for cheaper ways to foster interdisciplinarity. One common approach from the mid-­1970s onward was to establish facilities where equipment could be shared among, and passively encourage communication between, users from various disciplines. For instance, in 1973, NSF funded the Stanford Synchrotron Radiation Project as a “national user facility” attached to a high-­energy physics laboratory but made available to researchers from materials science, electrical engineering, and molecular biology (Hallonsten 2009). In the late seventies, Stanford administrators similarly sought funding from NSF’s new “Regional Instrumentation Facilities” program to make analytical instruments available to users from nearby universities and firms. These kinds of initiatives created possibilities for interdisciplinary encounters without mandating interdisciplinary collaboration. Those who successfully navigated the earlier era of more active and wide-­ ranging interdisciplinarity were sometimes well placed to move into the era of passive, narrower interdisciplinarity. For instance, after observing a five-­million-­ dollar NSF grant for a user facility for microelectronics at Cornell in 1977, John Linvill and James Meindl reacted with a proposal for a Center for Integrated Systems (CIS) the next year. In part, the CIS allowed Meindl’s Integrated Circuits Laboratory to expand in order to—­like the RIF program—­passively offer tools for use by a potentially multidisciplinary user base. But, like Linvill and Meindl’s earlier ventures, the CIS as a whole also actively “integrated” multiple disciplines: Electrical Engineering, Computer Science, and Physics. Yet the disciplines Meindl and Linvill worked with in the early seventies—­Education, Psychology, and the departments of the Medical School—­were, for the most part, missing from their narrower vision of interdisciplinarity in the 1980s. The story doesn’t end with retrenchment and thinning interdisciplinary ties, however. In the late eighties, one of the engineering students who had worked on the Optacon, Jim Plummer, succeeded Meindl as director of the ICL. In 1994, Plummer joined the ICL with Cornell’s facility to form a National Nanofabrication Users Network—­a consortium of facilities providing tools to a

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multidisciplinary user base. Through the nineties, biological research at the NNUN member facilities steadily increased. In 2004, the NNUN expanded and morphed into the National Nanotechnology Infrastructure Network, with life science applications pursued at most of its sites. Several NNIN facilities—­ including Meindl’s Nanotechnology Research Center at Georgia Tech—­ primarily specialized in connecting nanofabrication with biomedicine. Similarly, Calvin Quate turned away from acoustic microscopy in the early eighties and toward what turned out to be one of the central tools of nanotechnology, the scanning tunneling microscope. That turn was motivated in part by the STM’s potential to improve on the microelectronics quality control capability of the acoustic microscope. Yet the old desire to contribute to the life sciences was still there. In 1985, Quate and two IBM scientists coinvented a variant of the STM, the atomic force microscope, that is today widely used in the microelectronics industry, in nanotechnology research, and in the life sciences. In Quate and Meindl’s case, the changes wrought in the Vietnam era never entirely went away. Even if their eighties research hewed close to microelectronics, they remained cognizant of opportunities to return to the life sciences. After the Cold War, and the expansion of NIH funding in the nineties, those opportunities presented themselves. A similar picture could be painted for Stanford as a whole, and indeed for many American research universities. The diverse and ambitious aspirations attached to interdisciplinarity in the late sixties were deemphasized in the Carter, Reagan, and first Bush eras. Later national turning points, however, have brought back calls for a more interdisciplinary and problem-­oriented university. If those calls are to be answered with thoughtful, reasoned responses, then Stanford circa 1970 should be a guide. Vague but urgent calls for interdisciplinarity can assuage dissent, lure funders, and generate real insight. But responding to vague calls for interdisciplinarity can also lead researchers to waste time and resources and jeopardize careers (e.g., Chowning). Pressure for interdisciplinarity can defuse and heighten campus tension. Opposition to such pressures can be entirely reasonable, but skepticism about interdisciplinarity can also lead to countervailing pressures for immediate results that are unrealistic (e.g., Rambo). Then again, years of attempting interdisciplinarity without positive results can lead to rejection of once-­lofty goals (e.g., Quate). There are no easy answers. references

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Belanger, Dian. 1998. Enabling American Innovation: Engineering and the National Science Foundation. West Lafayette, IN: Purdue University Press. Berkenstock, H. R., Jr. 1977, June 13. Letter to Neils Reimers, Box 1, Folder “American Optical Corporation,” Calvin Quate Papers, SC 347 (83-­033)—­1987 Accession, Department of Special Collections and University Archives, Stanford University Libraries. Buchanan, Bruce G. 1983. “Introduction to the COMTEX Microfiche Edition of Memos from the Stanford University Artificial Intelligence Laboratory.” AI Magazine 4 (Winter): 37–­42. Campus Report. 1968a. “Educational Improvement Plan Possible under Ford Grant.” Campus Report, September 25. ———. 1968b. “First SES Reports Released: Continuous Process Hoped For.” Campus Report, November 26. ———. 1970a. “Defense Research Will Shift If Forced out of Universities.” Campus Report, September 23. ———. 1970b. “Dr. Miller: Society Should “Strike Balance” in Research Activities.” Campus Report, November 25. ———. 1970c. “Private Gifts during 1969–­70 Top $30 Million; 19,000 Donors.” Campus Report, September 23. ———. 1970d. “Research Exposition Is Planned; Organizational Meeting Tomorrow.” Campus Report, December 9. ———. 1971a. “Lyman Looks at Future: Toward a More Open University.” Campus Report, April 14. ———. 1971b. “Research Exposition Is Planned.” Campus Report, January 27. ———. 1971c. “Revolt Against Reason Can Have ‘Ominous Consequences.’ ” Campus Report, January 27. ———. 1971d. “Values, Technology, and Society Included in Experimental Program.” Campus Report, May 19. Chowning, John M., Leland C. Smith, and Albert Cohen. 1974, June  18. “The Computer Music Facility: A New Medium.” Proposal submitted to the National Endowment for the Arts in conjunction with the proposal “Computer Simulation of Music Instrument Tones in Reverberant Spaces” submitted to the National Science Foundation. Coburn, G. C. 1976, May 18. Letter to Quate, Box 1, Folder “Contract—­Hartford Foundation, Inc., Correspondence—­Proposals (1970–­1981),” Calvin Quate Papers, SC 347 (83-­033)—­ 1987 Accession, Department of Special Collections and University Archives, Stanford University Libraries. Cohen-­Cole, Jamie Nace. 2003. “Thinking about Thinking in Cold War America.” PhD dissertation, Princeton University. Colyvas, Jeannette. 2007. “From Divergent Meanings to Common Practices: Institutionalization Processes and the Commercialization of University Research.” PhD dissertation, Stanford University. DOD Sponsored Research at Stanford. 1971, June. “Two Perceptions: The Investigator’s and the Sponsor’s. A Report by a Stanford Workshop on Political and Social Issues.” Vol. 1. Donham, Nancy. 1972, July 10. Stanford University News Service Release on the Center for Interdisciplinary Research, Box 14, Folder Center for Interdisciplinary Research (CIR), 1972–­1974, Stanford University Sponsored Projects Office Papers, SC 344, Department of Special Collections and University Archives, Stanford University Libraries. Hallonsten, Olof. 2009. “Small Science on Big Machines: Politics and Practices of Synchrotron Radiation Laboratories.” PhD dissertation, Lund University.

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Kaiser, David. 2011. How the Hippies Saved Physics: Science, Counterculture, and the Quantum Revival. New York: Norton. Kenney, Martin. 1986. Biotechnology: The University-­Industrial Complex. New Haven, CT: Yale University Press. Kompfner, Rudolf. ~1971a. Notes on “Generalities about a National Science Policy,” Box 1, Folder 1 (Allocation of Research Funds), Rudolf Kompfner Papers, SC 194 ACCN 85-­ 074, Department of Special Collections and University Archives, Stanford University Libraries. ———. ~1971b. Untitled essay on music, cognition, and technology, Box 1, Folder “Music Personal Reminiscences,” Rudolf Kompfner Papers, SC 194 ACCN 85-­074, Department of Special Collections and University Archives, Stanford University Libraries. ———. 1975. “Recent Advances in Acoustical Microscopy.” British Journal of Radiology 48: 615–­627. ———. 1976, February 5. Notes for NSF Review of RANN Contract, Box 3, Folder “Scanning Acoustic Microscope, 1974–­76,” Rudolf Kompfner papers, SC 194 ACCN 85-­074, Department of Special Collections and University Archives, Stanford University Libraries. Lécuyer, Christophe. 2006. Making Silicon Valley: Innovation and the Growth of High Tech, 1930–­1970. Cambridge, MA: MIT Press. Lehrman, Paul. 2005, February 1. “A Talk with John Chowning.” Mix. http://www.mixonline .com/mag/audio_talk_john_chowning/. Lenoir, Tim, and Eric Giannella. 2006. “The Emergence and Diffusion of DNA Microarray Technology.” Journal of Biomedical Discovery and Collaboration 1: 11. Leslie, Stuart W. 1987. “Playing the Education Game to Win: The Military and Interdisciplinary Research at Stanford.” Historical Studies in the Physical Sciences 18: 55–­88. ———. 1993. The Cold War and American Science: The Military-­Industrial-­Academic Complex at MIT and Stanford. New York: Columbia University Press. ———. 2010. “‘Time of Troubles’ for the Special Laboratories.” In Becoming MIT: Moments of Decision, edited by David Kaiser, 123–­144. Cambridge, MA: MIT Press. Leslie, Stuart W., and Robert H. Kargon. 1996. “Selling Silicon Valley: Frederick Terman’s Model for Regional Advantage.” Business History Review 70 (Winter): 435–­472. Linvill, John G. 1966, December 16. Memo to Calvin Quate, binder “Electrical Engineering,” Calvin Quate Papers, SC 347 (83-­033)—­1987 Accession, Department of Special Collections and University Archives, Stanford University Libraries. ———. 1967, January 26. Memo to Committee for the Study of Stanford’s Educational Program, Re: Comments of Electrical Engineering on Stanford’s Educational Program and Objectives, binder “Electrical Engineering,” Calvin Quate Papers, SC 347 (83-­033)—­1987 Accession, Department of Special Collections and University Archives, Stanford University Libraries. ———. 1973, March. “Research and Development of Tactile Facsimile Reading Aid for the Blind.” Final Report for Grant No. OEG-­0-­8-­071112-­2995. Lowen, Rebecca S. 1997. Creating the Cold War University: The Transformation of Stanford. Berkeley: University of California Press. Lyman, Richard W. 2009. Stanford in Turmoil: Campus Unrest, 1966–­1972. Stanford, CA: Stanford University Press. Massy, William F. 1972, April. Draft proposal for a Center for Interdisciplinary Research, Box 14, Folder 9, Sponsored Projects Office Records, SC 344 ACCN 1986-­200, Department of Special Collections and University Archives, Stanford University Libraries.

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“A Message from the Stanford Sit-­In.” 1969, April. Box 10, Folder AEL Sit-­In April 1969, Stanford University Sponsored Projects Office Collection, SC 344, Department of Special Collections and University Archives, Stanford University Libraries. Miller, William. 1975, May 13. Letter to Joseph Danek, Box 23, Folder National Science Foundation, Misc., Richard Lyman Presidential Papers Series 3, SC 215, Department of Special Collections and University Archives, Stanford University Libraries. Mody, Cyrus C. M., and Andrew J. Nelson. 2013. “‘A Towering Virtue of Necessity’: Computer Music at Vietnam-­Era Stanford.” Osiris 28: 254–­277. Mowery, David C., Richard R. Nelson, Bhavan Sampat, and Arvids A. Ziedonis. 2004. Ivory Tower and Industrial Innovation: University-­Industry Technology Transfer before and after the Bayh-­Dole Act in the United States. Stanford, CA: Stanford Business Books. National Science Foundation. 1978. “The Promise of Acoustic Microscopy.” Mosaic, March/ April, 35–­41. Nelson, Andrew J. 2005. “Cacophony or Harmony? Multivocal Logics and Technology Licensing by the Stanford University Department of Music.” Industrial and Corporate Change 14: 93–­118. O’Mara, Margaret Pugh. 2005. Cities of Knowledge: Cold War Science and the Search for the Next Silicon Valley. Princeton, NJ: Princeton University Press. Quate, Calvin F. 1983, April–­June. Notebook, Box 2, Calvin F. Quate Papers, SC 347 Accession ARCH-­2004-­117, Department of Special Collections and University Archives, Stanford University Libraries. Quate, Calvin F., Abdullah Atalar, and H. K. Wickramasinghe. 1979. “Acoustic Microscopy with Mechanical Scanning—­A Review.” Proceedings of the IEEE 67: 1092–­1114. Rambo, William. 1970, July 23. Speech, Box 19, Folder Pres. Pitzer Dinner, William R. Rambo Papers, SC 132, Department of Special Collections and University Archives, Stanford University Libraries. Smith Hughes, Sally. 2001. “Making Dollars out of DNA: The First Major Patent in Biotechnology and the Commercialization of Molecular Biology, 1974–­1980.” Isis 92: 541–­575. Stanford-­NASA Memorandum of Understanding. 1968, April. Box 125, Folder National Aeronautics and Space Administration, Richard Lyman Presidential papers Series 1, SC 215, Department of Special Collections and University Archives, Stanford University Libraries. Stanford University Electronics Research Review. 1969, August 12. Vol. 11. Stanford Electronics Laboratories, Collection 3120/4 STAN, Department of Special Collections and University Archives, Stanford University Libraries. Students for a Democratic Society (Stanford chapter). 1968a, December. “Welcome Pitzer!!” Box 6, Folder 10 (Student Unrest, 1968–­2), William R. Rambo Papers, SC 132, Department of Special Collections and University Archives, Stanford University Libraries. ———. 1968b. Through the Looking Glass: A Radical Guide to Stanford. Box 6, Folder 9 (Student Unrest, 1968-­1), William R. Rambo Papers, SC 132, Department of Special Collections and University Archives, Stanford University Libraries. Sturgeon, Timothy J. 2000. “How Silicon Valley Came to Be.” In Understanding Silicon Valley: The Anatomy of an Entrepreneurial Region, edited by Martin Kenney, 15–­47. Stanford, CA: Stanford University Press. Vettel, Eric James. 2006. Biotech: The Countercultural Origins of an Industry. Philadelphia: University of Pennsylvania Press. Yi, Doogab. 2008. “The Recombinant University: Genetic Engineering and the Emergence of Biotechnology at Stanford, 1959–­1980.” PhD dissertation, Princeton University.

9 ◆ Interdisciplinarit y Reloaded? Drawing Lessons from “Citizen Science” B a r b a r a P r a i n s a c k a n d H au k e Ri e s c h

The idea that laypeople make significant contributions to scientific research, not by means of being objects of research but by acting as researchers themselves, is attracting increasing attention within academic and public discourse (e.g., Silvertown 2009; Nielsen 2012; Shirk et al. 2012). In this chapter we will take a closer look at the practices that are subsumed under labels such as citizen science (CS), as well as the rhetoric mobilized around it. The current celebration of the “emergence” of CS, which tends to emphasize novelty and disruption at the cost of continuity and conservation, allows us to draw important lessons for the study of interdisciplinarity as a social and political phenomenon. Both interdisciplinarity and CS are imbued with the hope that they will change science in radical ways and redeem it from some of its shortcomings, such as institutional conservatism, overreliance on credentialed authority, as well as epistemic inertia. We argue that these aspirations are, first and foremost, fantasies that CS and interdisciplinarity, for all the advantages and benefits that they bring, are institutionally and structurally unable to fulfill. At the same time, these fantasies are real in that they articulate preferences regarding what types of collaboration and research are valuable (and valorizable).

The Many Lives of Citizen Science Scientists, science writers, and those keen on promoting further and meaningful public engagement with science seem to be intrigued by the phenomenon that 194



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people without professional training contribute not only to the regulation of science, but to some of the core activities of scientific knowledge production. This phenomenon has been discussed under labels such as CS, public participation in scientific research (Shirk et al. 2012), or similar notions. CS—­which we use here to refer to any project or initiative that includes people without professional training in a pertinent subject in the creation of scientific knowledge—­is often seen to fulfill a dual function: The first is to enable or advance scientific research in ways that established science finds difficult to pursue. CS may be seen as “better” due to the lack of resources within established institutions, or because the crowdsourcing of certain tasks allows data collection, data analysis, or other aspects of scientific knowledge production to be done in a faster or more efficient way.1 These ambitions bear a striking resemblance with arguments made in favor of interdisciplinarity, where pooling of resources, cooperation between different groups, and the importance of outside perspectives have been suggested to “improve” science (Nissani 1997). Second, for participating members of the public, which a project may recruit by word of mouth, online, or through community gatekeepers, CS is often seen to offer new opportunities to contribute to actual scientific research. “Laypeople” are assumed to want to participate just for fun, for the opportunity to learn more about science, to contribute to research on a disease that they or people close to them suffer from, often from their own home or back garden. In some cases, participation is driven by the desire to create alternative ways of knowing; in community-­based participatory research, for example, citizens and professional experts collaborate in designing and conducting research that addresses acute needs of underserved communities (Brown and Mikkelsen 1997; Brown et al. 2004; Brown 2013). An added benefit is, in the views of some actors and commentators, that “citizen scientists” learn not only about the subject area to which they are contributing, but more generally about how science works. In fact, within science education, learning about the “nature of science” has been identified as a key component of transmitting science knowledge (e.g., Bell et al. 2001; Abd-­El-­ Khalick et al. 1998). It is seen to equip students and the public at large with a working appreciation of the limitations and uncertainties of new and controversial scientific developments. This, in turn, is seen to enhance their capacity to contribute to the democratic processes of science governance (Kolstø 2000). These aspirations of offering supposedly radically new ways of doing science, by asking questions that were never asked before, by collecting data in new ways, or by bringing different groups together, also mirror some of those hopes associated with interdisciplinarity. Because of being seen as meeting the objectives of scientific knowledge creation and public engagement simultaneously, CS has often been portrayed as a

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win-­win situation. Within this narrative, CS makes meaningful and “truly” participatory science engagement activities more worthwhile for scientists to organize or facilitate: CS enables professional scientists to expedite their research and engage with the public at the same time. CS is thus portrayed as ticking not only one but several boxes on professional scientists’ career development plans. One of the most notorious objections that scientists have about spending time on public engagement—­namely that it detracts from their scientific work—­can be sidestepped in this manner. Through its inherent promise to enhance both, the democratization of science and public engagement with science (as demonstrated by sociologists of science communication in the early 1990s; e.g., Wynne 1992, 1993), CS also represents what we call a Wynne-­win situation: it successfully navigates the often contrasting demands from within the academic Public Understanding of Science (PUS) literature (Miller 2001). This literature wants science engagement to be an informal science education exercise, foregrounding informal science learning (therefore representing an implicit deficit model of PUS; Bell et al. 2009). At the same time it should foster scientific citizenship ideals that emphasize genuine dialogue and contextual engagement that arose from Wynne and Irwin’s stringent criticism of the deficit model (Irwin and Wynne 1996). However we turn it, CS is hoped to produce “better” science, however differently “better” is understood. Also in this sense, there is a striking parallel between CS and interdisciplinarity: they are both imbued with the promise to lead to better outcomes than traditional institutionalized science, and mono-­ disciplinary knowledge respectively. In both cases, the existing literature focuses mostly on discussions of how interdisciplinarity and CS can be done best, and includes many more celebratory than critical accounts (although unlike in scholarship on interdisciplinarity, an important stream in discussions on CS focuses on how CS can be done ethically; e.g., Vayena et al. 2015; Resnik et al. 2015). In the next section, we will explore configurations and goals of CS projects against the expectation that CS provides something radically new, and something better than allegedly mainstream, institutionalized science. We will argue that important lessons from this can be drawn for critical studies of interdisciplinarity, where a similar dynamic could be observed. The argument that there are disciplinary “silos” that foster self-­referential engagements with issues relevant mostly to one’s immediate peers but often ignorant of larger social and political aspects ( Jacobs 2013, 18–­20, and 96) portrays “traditional” research as unduly homogenous and stable. The same rhetoric presents interdisciplinarity as a silver bullet to rescue monodisciplinary science from its slumber and make it fit to address the world’s “real” problems. The value in drawing comparisons between the rhetoric versus practice of interdisciplinarity on the one hand and the rhetoric versus practice of CS on the other makes visible an important function of both visions as redemptive fantasies. Both allow for a temporary escape into a



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better world. As we argue in our conclusion, drawing upon scholarship in the sociology of expectations (Brown and Michael 2003; Borup et al. 2006), these visions, however, are “real” in that they shape our understanding of what valuable research is, and who we should collaborate with.

Histories and Stakes of CS First of all, let us take a brief look at the history of the concept of CS. “Amateur” science—­namely contributions that non–­professionally trained people make to scientific advance—­is as old as science itself (e.g., Shuttleworth and Frampton 2015). The great popularity of the notion of CS is a much younger phenomenon. Perhaps we should use the plural term “notions,” here, as the meaning of CS is far from uniform. To start with, the term was invented twice, seemingly independently, in the mid-­1990s: In the United States, Rick Bonney at the Cornell Lab of Ornithology, and cofounder of the lab’s CS program, started using the CS label to refer to informal science communication projects aiming to teach science by getting people involved in it (see Bonney et al. 2009). In Europe, STS scholar Alan Irwin used CS as a label for his idea of scientific citizenship (Irwin 1995). Irwin’s understanding of CS is different from Bonney’s in the sense that Bonney focuses on contributions that nonprofessionals make to the core activities of scientific knowledge production and within the epistemic commitments of mainstream institutionalized science. Irwin and his followers, in contrast, are more interested in the democratic governance of science, that is, in activities that go beyond (and are often outside of) the creation of scientific knowledge in the narrow sense of the word. Their concerns include public participation in the ethical and legal regulation of science, as well as questions about legitimacy. The key concerns within these two traditions of CS are thus different, but the scholarship of these traditions overlaps, especially where hybrid forms of CS are envisaged where the hopes of (Irwin-­style) CS are attached to fairly traditional, deficit-­reducing (Bonney-­style) CS projects. Both within academic scholarship and within public discourse, a broad variety of projects are subsumed under the label of CS. Classification attempts have been made along dimensions such as who is the main driver behind the project (i.e., scientists or communities; see Bonney et al. 2009 and Shirk et al. 2012), or according to whether the project is based primarily on face-­to-­face interaction or the Internet (Wiggins and Crowston 2011). Projects can also be grouped according to the main goal of the project, which can be either data collection and scientific knowledge production (such as in Galaxy Zoo; www.galaxyzoo .org), traditional public engagement (such as most of the Cornell Lab of Ornithology group of projects; www.birds.cornell.edu), or policy-­driven public engagement (such as the Department for Environment, Food & Rural Affairs/

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Forestry Commission [n.d.] “Action Plan for Tree Health and Plant Biosecurity”). Another way of classifying CS projects considers how they are organized methodologically, that is, where the main added value of the public contribution lies: in a large number of participants carrying out easy tasks, or in a small number of highly skilled contributors investing a lot of time and effort into the project. An example for the former are crowdsourcing-­style projects that rely on the large numbers of members of the public that can be mobilized to help the project by performing small and nonspecialized tasks that require relatively little training—­such as classifying galaxies, counting earthworms, or solving simple puzzles. A large pool of participants can also enhance geographical coverage, for example in environmental monitoring projects where it would not be feasible for the scientists to cover all corners of the country themselves, as in the case of biodiversity projects. Individual members of the public can also enhance the coverage of the project by working on spaces that are often off-­limits to research scientists, such as private backyards and gardens. For projects that rely on intense contributions from a smaller number of highly skilled participants, the analysis of bird ringing in the Open Air Laboratories (OPAL; see the “Open Air Laboratories (OPAL)” section) is an example. Here, laypeople perform more specialized tasks that involve stronger commitment in terms of attending training sessions and also greater commitments in their everyday lives (such as spending every morning of turtle hatching season on the beach, as in the project described by Cornwell and Campbell 2012). Also platforms such as Kaggle (www.kaggle.com) use online crowdsourcing to find the few people possessing the right, highly specialized expertise to address specific “hard” tasks. These latter types of projects do not normally recruit very large numbers of active participants because of the resource and time constraints of both the science and public sides of the project, or because they rely on the individual lay expertise that only selected or committed members of the public can bring along. Finally, a third type of project that has often been labeled CS, or at least cited as a successful forerunner of CS, is that performed by lone “amateur scientists.” Silvertown (2009), for example, mentions the historical example of Benjamin Franklin, who was one of the many “gentlemen” who have taken up science on their own and therefore became “citizen scientists.” In this context, some writers, usually employing Irwin’s understanding of CS, also cite Epstein’s (1996) study of expertise among AIDS activists as an instance of CS, often as a way of demonstrating to potentially skeptical readers that (at least initially) untrained members of the public can make a valuable and lasting contribution to the scientific process. Other precursors of CS were mass participation projects in the nineteenth and early twentieth centuries by natural history societies that relied on amateur membership for regular monitoring; these were particularly active in ornithology and include initiatives such as the Audubon Society Christmas bird count



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and the Royal Society for the Protection of Birds bird atlas (see Greenwood 2007 and Toogood 2011 for a history of these movements). Still going strong today, such initiatives continue to rely on their large membership base of enthusiastic amateurs. They were not, however, explicitly interested in engaging the wider public, promoting science learning, or raising environmental awareness. Instead they were reaching toward members of the public who, through their involvement in these societies, already were aware, well informed, and enthusiastic about the topics covered. It is only fairly recently, with the rise of public engagement projects as a mission for natural history societies, that mass participation ornithology initiatives have morphed into CS projects with a strong emphasis on public engagement. Many different stakeholders are involved and supposed to benefit from CS, including individual scientists, science at large, members of the public, the public at large, science educators, and science policy enthusiasts. This broad group of imagined beneficiaries of CS can lead to misunderstandings, clumsy compromises, and unrealistic expectations about the nature and goals of a particular CS project. Many CS projects struggle if they are seen as not delivering satisfactorily to all constituents. Instead of a truly boundary breaking concept that sets all these stakeholders up to agree on a central philosophy, CS can end up as an intellectual Rorschach test (see Williams et al. 2005) where science communicators see an opportunity for good old-­fashioned deficit-­reducing science education, whereas science policy people spot opportunities for informative “upstream” dialogues, and members of the public enjoy a fun day out. Some professional scientists may see it as a way to get their hands on free labor, while others might regard CS as threatening their own position. Conflicting understandings and interpretations can end up pulling CS into different and contrary directions because all these stakeholders have different ideas of what constitutes success. Similarly, what may be a good science-­policy-­informing project may be mediocre science, what might be a fantastic learning opportunity for laypeople may not be a particularly fun activity pulling in enough participants, and what might be a time-­saving crowdsourcing project for a professional scientist may lead to poor policy making. A similar pull into different directions—­because of the different expectations from various participating groups—­may also be evident in interdisciplinary projects.

The Not-­So-­Disruptive Effects of Citizen Science While a CS project run by patients who organize their own medical studies and who are bypassing professional medical experts disrupts some aspects of clinical research practice and institutions,2 other CS projects attempt to do what established professional science scientists do, but better. “Better” can mean that the

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project collects more data, collects data faster, or speeds up and improves data analysis or the dissemination of results. This can take place without the slightest challenge to any of the steps or the value commitments and contingencies underpinning established institutional scientific knowledge production. Examples for such CS projects are manifold; they include cases from where participants are collecting data that would take professionals too long to collect, or where citizens do transcription or classification work for which they receive basic training by a web tutorial, for example (e.g., http://www.oldweather.org/). In some self-­ proclaimed CS projects, participants are asked to download a smartphone app that will aid data collection; they are thus turned into little more than a remote sensor for the project (e.g., http://www.nssl.noaa.gov/projects/ping/). Despite the rhetoric about CS being tantamount to “reinventing discovery” (Nielsen 2012), it is thus apparent that many CS projects do not pose challenges to institutionalized science. By helping to make science “better,” some may even reinforce the current mode of producing knowledge and goods intact.3 As has become clear in some of the examples discussed earlier, in some instances the crowdsourcing of tasks that professional scientists used to do could be seen as a cost-­saving strategy. Even where this is not an explicit intention, some variants of CS resonate with neoliberal values not only because of their emphasis on greater efficiency or greater self-­determination (see Topol 2015), but also because of the free labor that participants are expected to provide. As members of the Citizen Participation in Science and Medicine Network at King’s College London—­a group of academics committed to critical scholarship on this topic—­put it, CS is especially problematic “when the promotion of citizen involvement is an expression of the erosion of government responsibilities and capacities of collective deliberation and control” (CPSM 2014). Rather than disrupting anything, CS, here, helps to keep a broken system going. For social scientists studying CS projects, this has two main implications: First, when analyzing the goals of a CS project, we need to examine the ways in which the project sets out to achieve “better” results than institutionalized professional science. Does it attempt to generate more data, or data with greater validity? Does it aim to be better at considering societal issues? Does it try to be more democratic? Does CS—­as many “biohackers” and do-­it-­yourself (DIY) biology affiliates understand their own work (Delfanti 2013)4—­represent an attempt to free science from the moral and practical constraints of established research institutions that have been corrupted by corporate interests? And do different types of participants in the project share the same goals and values? If not, how do their understandings of the project’s goals differ, and why? The second implication is that we should look at both the disruptive and the conservative effects of CS projects; most projects contain elements of both (Prainsack 2014).



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Adding to the potential problems of projects being pulled into different directions we can also point to potential issues arising when the publicly created science is not as universally welcomed by a wider, possibly more conservative, scientific community. This could have detrimental effects for the careers of professional scientists organizing or participating in CS projects. Furthermore, there can be unresolved ethical issues surrounding the use of public data and potentially outsourcing the jobs of scientific research, which in turn may foster resentment on parts of the scientific community. These issues and tensions could also make some of the more rushed and unreflective CS projects vulnerable to criticism of the quality or ethicality of their research practices (see Riesch and Potter 2014). CS is clearly a contested and still evolving concept and practice. As a label it is currently attached to a multitude of different ideas, options, and expectations. All these different definitions and interpretative traditions of CS, as well as the different potential and real stakeholders all with their own aims, goals, and methods of doing CS, make the analyst’s job of trying to pin down CS as a coherent object of study very difficult. “Cleaning” our definition of CS for the purposes of this chapter, however, would lose exactly this conflict over boundaries, methods, and control over the mission and goals of a project that makes CS such a fruitful field for comparison with interdisciplinarity: the similarities lie precisely in the vagueness of the idea, the multitude of aims, goals, and understandings that a variety of actors bring both to specific CS and interdisciplinarity projects and to the concepts in general. In the following section we will introduce two cases that embody two very different ways of doing CS. First, they differ in terms of their stated goals: one focuses on public engagement, while the other seeks to enable research in an area where traditional research funding would be difficult to obtain. Second, they differ in how professional scientists interact with nonprofessionals: the first project is based to a large part on face-­to-­face and physical interaction, while in the second interaction takes place via an online platform. Third, they are also located in different scientific domains, environmental and biomedical science, respectively. Finally, they are different in terms of internal diversity: one is a cluster of specific CS projects that cover different methodological styles of CS, while the other one is essentially just one large project. The differences between the two cases will help us to pinpoint several complementary conclusions about how interdisciplinarity is managed by participating stakeholders, and how parallels can be drawn between interdisciplinarity and CS. There is, however, one important similarity between the two projects that we should highlight upfront: Neither of the examples discussed here represent community-­led projects. It is professional scientists in the first case and private entrepreneurs in the second case who conceived the project and invited

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members of the public to participate (for more detailed discussions of different forms of agency within CS projects, see Prainsack 2014). This situation, however, is not atypical for the wider field of CS, where many projects that started out as citizen-­led initiatives were taken over by organizations or commercial enterprises later on (Prainsack 2014). Grassrootedness is difficult to scale and manage. The Open Air Laboratories (OPAL)

The Open Air Laboratories (OPAL) project is a large-­scale CS-­style public engagement project that has been running in England since 2007. The goals that OPAL has set itself are to become an educational program that is accessible to all, to inspire a new generation of environmentalists, and to encourage an outdoor lifestyle as benefits for the participants, but also to produce meaningful and important new science and scientific understanding of the environment, and to encourage stronger partnerships between the community and the voluntary and statutory sectors (Davies et al. 2013). It is thus through increasing public knowledge and awareness of environmental issues that OPAL hopes to also contribute to the type of scientific citizenship ideals that characterize science policy-­led public engagement (see Irwin 1995). OPAL had originally been running five national surveys that cover earth, water, and soil pollution, climate change, and biodiversity, and on the back of the success of these surveys expanded to two more covering biodiversity again and most recently, tree health. Interested members of the public can find instructions on the surveys at the OPAL website or have one mailed to them. The instructions are designed with the aim of enabling participants to carry out the survey without supervision by members of the public of any age or ability using only commonly available instruments. For example the OPAL soil survey asks members of the public to dig a pit twenty by twenty centimeters (ten centimeters deep) and count and identify the earthworms within the hole, along with details on the soil (type, pH, moisture, etc.) and locality (detailed instructions along with an earthworm identification guide are provided at the OPAL soil survey website; www.opalexplorenature.org/soilsurvey). Results can be entered on the project website, and those by the (professional) project scientists are available for research purposes. On the back of this, OPAL scientists have produced a large amount of publications and associated PhD theses (e.g., Seed et al. 2013; Bone 2013) as well as papers reflecting on methodological aspects of CS, such as data quality (Bates et al. 2015). In addition, nine regional university teams also run their own science projects; most of these also follow a broad CS model in that they recruit members of the public for scientific activities. These include, for example, a study of urban bird corridors that recruited a dedicated but fairly small team of members of



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the public who along the way received training for accredited bird-­ringer status, and who then took part in studies on urban bird movement and habitat use (see Davies et al. 2013, 60). The scientists and science communicators involved in OPAL, as well as many of the lay participants, see it as a big success; this is reflected also by the funders’ agreement to extend the project to a sixth and eventually seventh national survey. A closer look among the scientists showed some underlying tensions within OPAL. Some of these tensions are typical for large-­scale projects and not specific to the CS-­related aspects of the initiative. Other issues, however, relate to the ethical and epistemological dimensions of the CS-­related aspects of the projects specifically, including worries over peer acceptance of the research or attribution of credit in the form of possible coauthorship with public participants (Riesch and Potter 2014). Others relate to the often conflicting goals of OPAL having to be simultaneously rigorously scientific as well as meaningful for public engagement (Riesch et al. 2013). The pull in different directions that we have noted was something that some scientists and science communicators on OPAL also noted: some professional scientists were worried that university managers might think they are spending too much time on public engagement to the detriment of their scientific output, and there were worries that data collected by members of the public may not be good enough for peer-­reviewed science publications. We found also the opposite type of worries, namely that the public engagement aspect might suffer from overemphasis on scientific rigor. In short, even though OPAL tried hard to find the right balance between public engagement and scientific knowledge production, this turned out to be difficult in practice.5 Partly to allay worries about the acceptance of CS by a conservative scientific establishment, the control over OPAL has firmly remained in professional scientists’ hands. They devised the study, defined the interesting problems, and analyze data and publish results. OPAL’s management have also been clear from the start that the epistemic authority should rest with those who have the relevant expertise and qualifications. Thus, despite the programmatic commitment to being inclusive and bridging divides between science and “the public,” what the project has been able to achieve in this context has so far been very limited. The British Gut Project

The British Gut Project (British Gut 2014) launched at King’s College London, with considerable media attention, in fall 2014. British media hailed it as one of the first health CS projects in the United Kingdom; a press release issued by King’s College London called it “the UK’s largest open-­source science project” (King’s College London 2014).

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The project was developed by professional scientists in collaboration with American scientists initially at the University of Colorado, who then moved to the University of California, San Diego (see Del Savio et al. forthcoming). Its aim is to understand the microbial diversity of the human gut by crowdsourcing both sample collection and funding: the project calls upon member of the public to register, contribute money to cover analysis and sequencing costs to the project, and then post a stool sample. (It is also possible to just donate money and not provide a stool sample [see FundRazr n.d.]; in addition, the project is supported also by industry donors.) Samples submitted by volunteers go to the University of California, San Diego for analysis. Participants receive personalized results within about three or four months after posting their samples. The project’s online information form confirms to participants that the British Gut Project is strictly a research project and will not convey “clinical information related to your microbiome profile,” and that it strives to establish a widely accessible database: researchers and other participants all over the world will be able to access the data collected in this project (British Gut 2014).6 The contributions of “citizen scientists” in this project consist primarily of paying a fee for the analysis of their stool samples (the fee covers transfer, storage, analysis, sequencing). Some participants also follow dietary protocols that involve tracking their food intake and sharing the data with the project. It is thus a research study where participants contribute funding as well as data and samples. What sets it apart from many traditional research projects run by professional scientists, though, is that participants are entirely self-­selected; they respond to calls for participants in public media and on the King’s College London websites. After signing up and paying a fee, participants are sent a kit to collect stool samples that they then send to the laboratory. Moreover, unlike in most traditional cohort studies, participants are given access to their own results. The project mobilizes the rhetoric of personalizing diagnosis and health and lifestyle advice, as exemplified by the quote of project leader Tim Spector in the King’s College London press release: “Many differences between people may be genetic. Participants in the American Gut and now British Gut can test how different diets shape their bodies. This is an exciting time to map our own personalized microbes—­which appear to be key for health and longevity, but also for many common diseases. This is a great opportunity for British citizen scientists to find out about their own bodies and diets—­whilst also benefitting science” (King’s College London 2014). While participants will have access only to their own personal results, professionals will have access to all samples and data. The British Gut Project is one where funding from “traditional” channels would probably have been impossible to find; the field is too young and not established enough for an exploratory project to have attracted the resources necessary to establish a data resource. Here, the crowdsourcing of sample



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provision and funding seemed to have been a necessary precondition for the project to go ahead (Del Savio et al. forthcoming). In terms of the input that participants have in the design of the study, or any other substantive research-­ related questions, however, the project is very traditional; the agenda is set, and all analytical work is done by professional scientists in established institutions. Neither has the project yet had any effect on the configuration of disciplinary knowledge and practices.

In Search of Better Science: ID and CS as Fantasies of Redemption As explained at the beginning of our chapter, we chose CS as a subject because it offers relevant lessons for interdisciplinarity. First of all, similar hopes and aspirations are attached to both interdisciplinarity and CS—­we call these “fantasies of redemption.” Second, both CS and interdisciplinarity necessitate working across social boundaries of different groups whose norms, goals, and ways of working may not easily align. What is striking is that both CS and interdisciplinarity are imbued with the same, mostly implicit promise, namely that they will lead to better outcomes than traditional institutionalized science and mono-­disciplinary knowledge, respectively. As critical scholars, researchers, and practitioners, our response to this needs to be at least twofold: First, we need to challenge the assumption that “the other” that CS and interdisciplinarity are compared to, namely institutionalized science and mono-­disciplinary research, are monolithic and homogeneous entities that are an impediment to innovative, out-­of-­the-­box, creative research (see also Moore 2011 and the introduction to this volume). They are clearly not: mono-­disciplinary research in its pure form exists in only a few places ( Jacobs 2013), and even where it exists, the problems that it suffers from—­such as being self-­referential, methodologically rigid, or constraining creativity—­appear also in interdisciplinarity settings (where they are sometimes exacerbated by being hidden behind a veneer of openness and flat hierarchies; see, e.g., Albert et al. this volume). Similarly, the kind of institutionalized science that CS is often contrasted with in fact bleeds into, and sometimes even dominates, CS projects. In extreme cases CS is merely a data collection tool for professional science strapped for money. Second, critical scholarship should curb the current enthusiasm around both interdisciplinarity and CS and focus its attention instead on analyzing what need the ideal of a supposedly new kind of research is responding to. Are the ideals of interdisciplinarity and CS fantasies of redemption that enable us to pretend that we can leave behind established practices and institutions with their entrenched norms and assumptions of how things are done? Does our wish to

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elevate ourselves over the daily grind of following rules, meeting expectations, and speaking to others who were socialized in the same in-­group require that we create the fiction of a “revolutionary” break with the past? Are those some important roles that both interdisciplinarity and CS play in our societies? If we concluded that an important role of interdisciplinarity and CS was indeed to serve as redemption fantasies that—­if only in our imagination—­ release us from the constraints of our daily struggles, then a few things that puzzle or disturb us about both interdisciplinarity and CS are suddenly no longer so puzzling. That existing institutions do not reward interdisciplinary research as much as research that conforms to the epistemic commitments of one discipline does then start to make sense: if interdisciplinarity is primarily a fantasy, then it is by definition not something that takes place, or even should take place, in reality. Similarly, while critical scholars of interdisciplinarity are receptive to the tenacity of hierarchies between different epistemologies and disciplines also in contexts of interdisciplinarity research (e.g., Albert et al. 2015; Albert et al. this volume), treating interdisciplinarity as a collective fantasy of redemption helps to explain why existing hierarchies are kept in place: those at the top of the disciplinary hierarchy would lose this position if they diluted their valuable and domineering paradigms and methods with those of a less highly regarded discipline. It is similar to a situation where a celebrity has an affair with a commoner: They may make out, but only rarely do they move in with each other, or get married. If, on the other hand, a lasting marriage is achieved, we may see new in-­groups and new conceptions of their out-­groups being forged, instead of hierarchies being smashed (Riesch 2014). Even if the commoner—­that is, the person lower in the hierarchy—­gets to “marry up,” many will hold against her where she came from. Similar things could be said about CS. Earlier on in this chapter we emphasized that CS, despite the rhetoric of disruption and revolution that it is embedded in, has many conservative aspects. Many CS initiatives—­as we have also seen in our case studies—­reiterate and support, rather than challenge, the commitments and norms of mainstream institutionalized science (see Del Savio et al. forthcoming). If we treat CS as a fantasy of overcoming the constraints and economic and political contingencies that institutionalized science is embedded in, however, then this appears as a necessity: CS is hoped to provide the new generation of creative and innovative scientists and researchers who will produce supposedly better science, but not by opting out entirely. A very similar diagnosis can be made about interdisciplinarity (see the introduction to this volume). If we did indeed treat CS and interdisciplinarity as fantasies, what follows from this in terms of our own practices of critical scholarship? Should we oppose calls for CS and interdisciplinarity? Should we focus on analyzing their



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conservative versus their disruptive potential, to counterbalance the dominance of celebratory accounts? Should we examine their counterintuitive and negative effects on academia and society as a whole? Perhaps we should do these things, but we should do one other thing for sure: we should explore what assumptions underpin concrete articulations of the hope that CS or interdisciplinarity create better science. In what sense are the outcomes of interdisciplinary research, or CS, hoped to be better than the outcomes of what is envisaged as “traditional” research? Are these outcomes hoped to be created in a more participatory or more democratic manner, as it is often assumed with CS? Does it mean that the knowledge produced in the new manner will be better validated? Does it mean that difficult problems can be tackled faster? And so forth. Answering these questions enables us to see the needs, hopes, and stakes of different actors more clearly, which in turn will help us to assess what other things may be at stake in a concrete situation. In the case of our OPAL case study, the production of better science is only one of several stated objectives, and not the most important either, with various public engagement aims dominating. This however changes as far as individual participating scientists are concerned, who for career-­and status-­related but also for personal reasons might place more emphasis on the science side of the project. Integrating these diverse objectives and aspirations into one coherent project typically entails many compromises. While the CS-­related nature of the project enabled collection of data on a scale that “normal” institutionalized research would not be able to achieve, there are clear limitations on what could be asked of untrained and inexperienced volunteers. At least in this sense, traditional boundaries between experts and public stayed very much in place, and—­in the case of OPAL—­consciously so, as the salaried scientists had control over design, analysis, and publication of the studies. In the case of the British Gut Project, the expectation of the project to generate “better” research means that it enables research where no public funding is available (Del Savio et al. forthcoming). This view, however, sidelines questions about the many ways in which CS may not be better than supposedly “traditional” science at all: sometimes with an even greater sampling bias and funded by industry, for example, some instances of CS could also be seen as a tool for esoteric or corporate agendas to expand their influence. Moreover, CS may also be less, not more, “democratic” than science in traditional research institutions. As we have seen, both the British Gut Project and OPAL were designed by scientists, organized and run in a top-­down manner, and participants have no or very little possibility to change the mission or terms of participation. With regard to the British Gut Project specifically, its main differences to an allegedly traditional institutional research project are that (1) participants not only are sample providers but also provide funding for the

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project, and (2) participants have access to the results of the analysis of their individual samples. If CS and interdisciplinarity are then reverting back to hierarchies and strict divisions between different constituencies, internally this might be the case because they serve an important purpose: clear divisions of responsibility and area of work mean that interdisciplinary and participatory projects can run smoothly. Externally it helps with gaining recognition within an often more conservative academic culture. Some of the hopes and ideals attached to interdisciplinarity and CS—­ including the aim to make research more collaborative, more relevant to real problems in the world, and more democratic—­are certainly worth striving for. It is a fallacy, however, to equate the current iterations of interdisciplinarity and CS with the fulfillment of these hopes: the fact that CS or interdisciplinary collaborations exist does not mean that the problems that CS or interdisciplinarity are hoped to solve are addressed automatically. Instead, we argue that interdisciplinarity and CS should be seen as fantasies that provide a vision of what a better science could or should look like. As stated earlier, this does not mean that therefore these hopes and fantasies are entirely ephemeral: They are real in the sense that they shape funding calls and understandings of valuable research; in the European context, the notion of Responsible Research and Innovation, for example, both includes a strong commitment to interdisciplinarity and places specific emphasis on collaborations between academics and commercial enterprises (von Schomberg 2011; Chadwick and Zwart 2013; Hilgartner et al. forthcoming). Interdisciplinarity also fosters a particular understanding of value and valorization of research that includes industry partners. Such an understanding is clearly apparent in the new research funding program of the European Commission, Horizon 2020, which poses much more emphasis on academic and industry collaboration than previous ones; it is also visible in the context of research evaluations in the United Kingdom, for example, where the notion of “research impact” is sometimes little more than a shorthand for financially valuable research outputs that are best produced in collaboration with (read: under the leadership of) commercial companies (see also Martin 2011). It is important to note that so far, both CS and interdisciplinarity have been underpinned and constrained by largely the same social dynamics as monodisciplinary institutionalized science. They entail the kinds of issues and challenges that are typical for any situation in which scientific knowledge production is carried out in a collaborative manner. Designing a successful interdisciplinary or CS project then means being aware of these dynamics, working with them, and using them to get the most out of the project, rather than seeing them as boundaries to be smashed.



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notes

We thank the Citizen Participation in Science and Medicine (CPSM) Network at King’s College London for many interesting discussions that have informed this chapter. Special thanks go to Mathieu Albert, Scott Frickel, Niccolo Tempini, and Victoria Vazquez for very helpful comments on this work. The usual disclaimer applies. 1   Examples of CS advancing science in a field or topic that would struggle to attract research funding from established sources are the American and the British Gut projects, studying intestinal microbes of volunteers (see Del Savio et al. forthcoming). A wide range of CS projects on topics such as bird watching and biodiversity and projects where volunteers classify images of galaxies (e.g., Zooniverse.org) illustrate that crowdsourcing is used to accelerate or broaden data collection or analysis. 2   We would like to refer here to Niccolo Tempini’s work on the PatientsLikeMe platform, a network for patients to exchange information about their conditions and treatments. The owners of the platform provide incentives for patients to upload data on various aspects of their lives and disease management. They turn unstructured data into structured data and sell access to patients, and aggregate data, to pharmaceutical companies and other corporate actors. Tempini found that disruption of existing clinical practices, while significant in respect to established institutional roles, loci, and practices, is not as serendipitous, bottom-­up, and self-­organized as it often seems. See Tempini (2015) and Kallinikos and Tempini (2014). 3   We gratefully acknowledge the very discussions in the Citizen Participation in Science and Medicine Network at King’s College London that have provided valuable input into this section. 4   DIY biology is a broad label that includes various groups and initiatives dedicated to freeing biology from the constraints of commercially corrupted institutionalized science and increasing its public benefit. DIY biology is sometimes discussed under other labels such as “garage biology” or “biohacking.” 5  More information about OPAL is available at the project website (http://www .opalexplorenature.org/), in Davies et al. (2011), as well as in the “community report” (Davies et al. 2013) and several of the scientific research papers that have been produced by OPAL so far (listed in the community report). 6   Both the American Gut Project and British Gut Project are part of the Earth Microbiome Project, which deposits all results online as soon as possible and prior to publication.

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Bell, Philip, Bruce Lewenstein, Andrew W. Shouse, and Michael A. Feder, eds. 2009. Learning Science in Informal Environments: People, Places, and Pursuits. Washington, DC: National Academies Press. Bell, Randy, Fouad Abd-­El-­Khalick, Norman G. Lederman, William F., McComas, and Michael R. Matthews. 2001. “The Nature of Science and Science Education: A Bibliography.” Science & Education 10 (1–­2): 187–­204. Bone, James. 2013. “Environmental Quality Management for Soil Protection: The Role of Citizen Science in the Process.” PhD dissertation, Imperial College London. Bonney, Rick, Heidi Ballard, Rebecca Jordan, Ellen McCallie, Tina Phillips, Jennifer Shirk, and Candie Wilderman. 2009. Public Participation in Scientific Research: Defining the Field and Assessing Its Potential for Informal Science Education. Washington, DC: Center for Advancement of Informal Science Education. Borup, Mads, Nik Brown, Kornelia Konrad, and Harro Van Lente. 2006. “The Sociology of Expectations in Science and Technology.” Technology Analysis & Strategic Management 18 (3–­4): 285–­298. British Gut. 2014. “Welcome to the British Gut Project!” http://www.britishgut.org/#entail. Accessed April 18, 2015. Brown, Nik, and Mike Michael. 2003. “A Sociology of Expectations: Retrospecting Prospects and Prospecting Retrospects.” Technology Analysis & Strategic Management 15 (1)(: 3–­18. Brown, Phil. 2013. Toxic Exposures: Contested Illnesses and the Environmental Health Movement. New York: Columbia University Press. Brown, Phil, and Edwin J. Mikkelsen. 1997. No Safe Place: Toxic Waste, Leukemia, and Community Action. Berkeley: University of California Press. Brown, Phil, Stephen Zavestoski, Sabrina McCormick, Brian Mayer, Rachel Morello-­Frosch, and Rebecca Gasior Altman. 2004. “Embodied Health Movements: New Approaches to Social Movements in Health.” Sociology of Health & Illness 26 (1): 50–­80. Chadwick, Ruth, and Hub Zwart. 2013. “From ELSA to Responsible Research and Promisomics.” Life Sciences Society and Policy 9 (1)(: 3. Citizen Participation in Science and Medicine Network (CPSM). 2015. “Joint Statement to Be Considered by the European on Ethics (EGE) at Their Meeting on the ‘Ethics of Citizen Involvement and Health’ 22 October 2014, Brussels, BE.” https://www.academia .edu/11735152/CPSM_Network_Position_Statement_on_the_Ethics_of_Citizen_ Involvement_and_Health. Accessed June 29, 2015. Cornwell, Myriah L., and Lisa M. Campbell. 2012. “Co-­producing Conservation and Knowledge: Citizen-­Based Sea Turtle Monitoring in North Carolina, USA.” Social Studies of Science 42 (1)(: 101–­120. Davies, Linda, J. Nigel B. Bell, James Bone, Martin Head, Laura Hill, Christopher Howard, Sarah J. Hobbs, David T. Jones, Sally A. Power, Neil Rose, Claire Ryder, Lindsay Seed, Gill Stevens, Ralf Toumi, Nikolaos Voulvoulis, and Piran C. L. White. 2011. “Open Air Laboratories (OPAL): A Community-­Driven Research Programme.” Environmental Pollution 159 (8–­9): 2203–­2210. Davies, Linda, Laura Gosling, Carolina Bachariou, Jennifer Eastwood, Roger Fradera, Nicola Manomaiudom, and Sallie Robins, eds. 2013. OPAL Community Environment Report: Exploring Nature Together. London: Imperial College London. http:// www.opalexplorenature.org/sites/default/files/7/file/Community-­E nvironment -­Report-­low-­res.pdf. Accessed August 17, 2015. Delfanti, Allesandro. 2013. Biohackers: The Politics of Open Science. London: Pluto Press.



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Del Savio, Lorenzo, Barbara Prainsack, and Alena Buyx. Forthcoming. “Crowdsourcing the Human Gut.” Journal of Science Communication. Department for Environment, Food & Rural Affairs/Forestry Commission. n.d. “Action Plan for Tree Health and Plant Biosecurity.” http://www.forestry.gov.uk/pdf/Action -­Plan-­Tree-­health-­and-­plant-­biosecurity.pdf/$FILE/Action-­Plan-­Tree-­health-­and-­plant -­biosecurity.pdf. Epstein, Steven. 1996. Impure Science: AIDS, Activism and the Politics of Knowledge. Berkeley: University of California Press. FundRazr. n.d. “British Gut.” https://fundrazr.com/campaigns/4sSf3/ab/5q J7f. Accessed August 13, 2015. Greenwood, Jeremy J. 2007. “Citizens, Science and Bird Conservation.” Journal of Ornithology 148 (1): 77–­124. Hilgartner, Stephen, Barbara Prainsack, and Benjamin Hurlbut. Forthcoming. “ELSI Research and Its Relation to STS.” In Handbook of Science and Technology Studies, edited by Clark Miller, Laurel Smith-­Doerr, Ulrike Felt, and Rayvon Fouche. Cambridge, MA: MIT Press. Irwin, Alan. 1995. Citizen Science: A Study of People, Expertise, and Sustainable Development. London: Routledge. Irwin, Alan, and Brian Wynne, eds. 1996. Misunderstanding Science? The Public Reconstruction of Science and Technology. Cambridge: Cambridge University Press. Jacobs, Jerry A. 2013. In Defense of Disciplines: Interdisciplinarity and Specialization in the Research University. Chicago: University of Chicago Press. Kallinikos, Jannis, and Niccolo Tempini. 2014. “Patient Data as Medical Facts: Social Media Practices as a Foundation for Medical Knowledge Creation.” Information Systems Research 25 (4): 817–­833. King’s College London. 2014. “King’s Launches British Gut.” http://www.kcl.ac.uk/newsevents/news/newsrecords/2014/October/Kings-­launches-­British-­Gut.aspx. Accessed April 18, 2015. Kolstø, Stein D. 2000. “Consensus Projects: Teaching Science for Citizenship.” International Journal of Science Education 22 (6): 645–­664. Martin, Ben R. 2011. “The Research Excellence Framework and the ‘Impact Agenda’: Are We Creating a Frankenstein Monster?” Research Evaluation 20 (3): 247–­254. Miller, Steven. 2001. “Public Understanding of Science at the Crossroads.” Public Understanding of Science 10 (1): 115–­120. Moore, Rob. 2011. “Making the Break: Disciplines and Interdisciplinarity.” In Disciplinarity: Functional Linguistic and Sociological Perspective, edited by Frances Christie and Karl Maton, 87–­105. London: Continuum. Nielsen, Michael. 2012. Reinventing Discovery: The New Era of Networked Science. Princeton, NJ: Princeton University Press. Nissani, Moti. 1997. “Ten Cheers for Interdisciplinarity: The Case for Interdisciplinary Knowledge and Research.” Social Science Journal 34 (2): 201–­216. Prainsack, Barbara. 2014. “Understanding Participation: The ‘Citizen Science’ of Genetics.” In Genetics as Social Practice, edited by Barbara Prainsack, Silke Schicktanz, and Gabriele Werner-­Felmayer, 147–­164. Farnham: Ashgate. Resnik, David B., Kevin C. Elliott, and Aubrey B. Miller. 2015. “A Framework for Addressing Ethical Issues in Citizen Science.” Environmental Science and Policy 54: 475–­481. Riesch, Hauke. 2014. “Philosophy, History and Sociology of Science: Interdisciplinary Relations and Complex Social Identities.” Studies in History and Philosophy of Science Part A 48: 30–­37.

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Riesch, Hauke, and Clive Potter. 2014. “Citizen Science as Seen by Scientists: Methodological, Epistemological and Ethical Dimensions.” Public Understanding of Science 23 (1): 107–­120. Riesch, Hauke, Clive Potter, and Linda Davies. 2013. “Combining Citizen Science and Public Engagement: The Open Air Laboratories Programme.” Journal of Science Communication 12 (3): A03. http://jcom.sissa.it/sites/default/files/documents/JCOM1203(2013)A03. pdf. Seed, Lindsay, Pat Wolseley, Laura Gosling, Linda Davies, and Sally A. Power. 2013. “Modelling Relationships between Lichen Bioindicators, Air Quality and Climate on a National Scale: Results from the UK OPAL Air Survey.” Environmental Pollution 182: 437–­447. Shirk, Jennifer L., Heidi L. Ballard, Candie C. Wilderman, Tina Phillips, Andrea Wiggins, Rebecca Jordan, Ellen McCallie, Matthew Minarchek, Bruce V. Lewenstein, Marianne E. Krasny, and Rick Bonney. 2012. “Public Participation in Scientific Research: A Framework for Deliberate Design.” Ecology and Society 17 (2): 29. Shuttleworth, Sally, and Sally Frampton. 2015. “Constructing Scientific Communities: Citizen Science.” Lancet 385: 2568. Silvertown, Jonathan. 2009. “A New Dawn for Citizen Science.” Trends in Ecology & Evolution 24 (9): 467–­471. Tempini, Niccolo. 2015. “Governing PatientsLikeMe: Information Production and Research through an Open, Distributed, and Data-­Based Social Media Network.” Information Society 31 (2): 193–­211. Toogood, Mark. 2011. “Modern Observations: New Ornithology and the Science of Ourselves, 1920–­1940.” Journal of Historical Geography 37 (3): 348–­357. Topol, Eric. 2015. The Patient Will See You Now: The Future of Medicine Is in Your Hands. New York: Basic Books. Vayena, Effy, Roger Brownsword, Sarah Jane Edwards, Bastian Greshake, Jeffrey P. Kahn, Navjyot Ladher, Jonathan Montgomery, Daniel O’Connor, Onora O’Neill, Martin P. Richards, Annette Rid, Mark Sheehan, Paul Wicks, and John Tasioulas. 2015. “Research Led by Participants: A New Social Contract for a New Kind of Research.” Journal of Medical Ethics. Published online before print. doi:10.1136/medethics-­2015-­102663. von Schomberg, Rene. 2013. “A Vision of Responsible Research and Innovation.” In Responsible Innovation, edited by Richard Owen, John Bessant, and Maggy Heintz, 51–­74. London: John Wiley. Wiggins, Andrea, and Kevin Crowston. 2011. “From Conservation to Crowdsourcing: A Typology of Citizen Science.” In Proceedings of the 44th Hawaii International Conference on System Sciences, 2764–­2773. Washington, DC: IEEE Computer Science Society. Williams, Robin, James Stewart, and Roger Slack. 2005. Social Learning and Technological Innovation: Experimenting with ICTs. Aldershot: Edward Elgar. Wynne, Brian. 1992. “Misunderstood Misunderstanding: Social Identities and Public Uptake of Science.” Public Understanding of Science 1 (3): 281–­304. ———. 1993. “Public Uptake of Science: A Case for Institutional Reflexivity.” Public Understanding of Science 2 (4): 321–­337.

10 ◆ One Medicine? Advocating (Inter)disciplinarity at the Interfaces of Animal Health, Human Health, and the Environment A n g e l a C a s s i dy

Since the mid-­2000s, international agencies, veterinary associations, NGOs, and funding bodies have issued calls of increasing frequency and volume advocating greater integration across the domains of human, animal, and environmental health. Citing threats to health from climate change, food insecurity, and emerging infectious diseases, alongside the similarity of disease processes across humans and animals, advocates have lobbied for the broader integration of health research, policy, and clinical practice, using slogans including “One World One Health” (Wildlife Conservation Society 2004), “One Medicine” (Schwabe 1984), and “One World–­One Medicine–­One Health” (Kahn et al. 2012). In recent years, “One Health” (OH) has been increasingly adopted as a catchall term by actors across a broadening range of scientific, medical, and professional disciplines, particularly veterinary medicine, global health, and infectious diseases. But what does OH actually mean? Where has OH come from, how is it used, who by, in what contexts, and how has it come to prominence in such a short space of time? Perhaps the most widely used working definition is the one put forward by the One Health Initiative, a U.S.-­based advocacy group including veterinarians, physicians, and public and environmental health professionals: “The One Health concept is a worldwide strategy for expanding interdisciplinary collaborations and communications in all aspects of health care for humans, animals and the environment” (Kahn et al. 2012). This definition is strikingly broad, promoting “interdisciplinary collaboration” without specifying who should be collaborating with whom and on what, or indeed how they should actually go about it. This is reflected in varying references to OH as a “concept,” as illustrated earlier, but 213

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also as an “approach,” a “movement,” and even a “paradigm.” In recent years, the language of OH has been adopted by a series of powerful actors in biomedicine and global health, including the U.S. government’s Centers for Disease Control and Prevention (CDC 2013), international organizations including the Food and Agriculture Organization (FAO), World Health Organization (WHO), and World Organization for Animal Health (OIE) (FAO et al. 2008, 2010), and the biomedical research funders Wellcome Trust (2010) and Gates Foundation (2013). Given the influence of these institutions in shaping health research, policy, and practice globally, it is important to understand why OH has had so much traction with these actors. Perhaps they have been convinced by the arguments—­even if the main priority is to improve human health, understanding why and how, for example, infectious diseases move between multiple species can bring obvious benefits. However, arguments about why we should think across humans and animals about health and medicine are far from new, and have been advanced from time to time ever since veterinary medicine emerged as a separate profession during the late eighteenth century (Woods and Bresalier 2014; Bresalier et al. 2015). Animals have regularly played important roles in the history of medicine, as bodies to experiment on, as sources of theoretical insight, and as objects of inquiry in their own right (Hardy 2003; Kirk and Worboys 2011). This raises an obvious question: given that ideas about the convergence of human and animal health have had such a long history, why have they gained significant international and institutional traction only so recently? In other words, the key question is not why OH, but why OH now. This chapter explores the recent emergence of OH as the self-­identified, broad-­based, interdisciplinary agenda we see today, and traces its origins in the histories of human and animal health, global development, conservation, and infectious diseases. It also investigates OH as an example of the increasing popularity of interdisciplinarity across changing academic, professional, and policy landscapes of the early twenty-­first century. What is the relationship between OH and other interdisciplinary agendas such as food security, what can this tell us about how these agendas are built, and why? Itself the outcome of an interdisciplinary collaboration,1 the research underlying this chapter has adopted a longitudinal, contemporary-­historical approach. It has investigated OH as part of ongoing interactions between scientific, professional, and policy spheres, while following the construction and spread of OH ideas and terminology over time and across multiple disciplines. The research has investigated a series of questions following from “why OH now?” What has happened to bring this agenda to the fore in biomedicine and global health, and to be adopted so widely over such a short period of time? Who are the key actors in this process, when did they become involved, and what does OH mean to them? What broader agendas

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and disciplinary interests are driving the uptake of OH? What forms of interdisciplinary and cross-­disciplinary partnership have been advocated, by which actors, and when?2 How have these discourses related to collaborative practice? Following the work of Jacobs and Frickel (2009), is interdisciplinarity in OH “bottom-­up” (generated by working researchers) or “top-­down” (imposed by institutions)? While the vast majority of publications discussing OH come from biomedical and health oriented authors, there is a small but rapidly developing social science and humanities literature on the topic. This has taken two main forms: social scientists adopting OH to work collaboratively with natural scientists on human and animal health research (e.g., Wood et al. 2012) and scholarship investigating OH itself. For the purposes of clarity, this chapter refers to OH as an “agenda,” and builds upon the latter body of work which, paradoxically enough, starts with studies of disciplinarity, the problems it can cause, and the reasons why OH actors reach out beyond their own disciplines. Several scholars researching OH have pointed toward mid-­2000s crises over threats of a global influenza pandemic as the most immediate driver (Scoones and Forster 2010). What these researchers and OH actors have described as the traditional disciplinary “silos” (tightly contained organizations) responsible for human health, animal health, food, and environment proved to be a major barrier to effectively managing diseases that moved freely across these domains ( Jerolmack 2013). The intensity of concerns over pandemic risks drove a well-­funded international response to these disease threats, which in turn drove greater cooperation across the silos of OH. During this process the international agencies FAO, WHO, and OIE adopted OH to signal their cooperative intent. Several studies have explored the breadth and conceptual flexibility of OH, as demonstrated earlier. While some have identified this flexibility as a key weakness, diffusing the idea beyond any useful meaning and acting as a barrier to further action (Lee and Brumme 2012), others have argued that this breadth enables OH to act as an “umbrella” under which OH actors can articulate a range of “slightly different visions” while working together (Leboeuf 2011, 64–­66). Developing this theme, Chien (2013, 223) concluded that the “productive vagueness” of OH enabled FAO, WHO, and OIE to move from mutually exclusive understandings of avian influenza toward a collective reframing that enhanced their interests, while minimizing interagency tensions. Chien drew upon Star and Griesemer’s (1989) classic study of “boundary objects” in scientific collaboration: concepts concrete enough to articulate common ideas across several “social worlds” (groups of people working toward a shared goal), yet flexible enough to be reinterpreted to fit the particular needs of each of these groups.3 Chien argued that OH acts as a boundary object for actors pushed into working together across the siloed social worlds of international health.

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However, as Star (2010) later discussed, boundary objects are not simply words with multiple meanings: they must be understood as the product of ongoing processes of social negotiation, and tend to operate at the scale of organizations, rather than in the details of interpersonal relationships or larger social structures. By focusing on the international health organizations, Chien’s analysis ably demonstrates how OH functions as a boundary object at a very specific “scope and scale” (Star 2010, 612–­613). However, by doing so, it cannot engage with how OH has been mobilized beyond this particular context, nor its relationship with scientific and medical practice more generally. Boundary objects function not only as collaborative tools, but also as markers of political negotiations between social worlds, especially in the case of academic disciplines. By using boundary objects strategically, individual and institutional actors can claim legitimacy, gain allies, and bring about changes in working practices (Löwy 1992). In her studies of the adoption and spread of molecular biology into cancer research during the 1980s, sociologist Joan Fujimura (1992, 1996, 1998) argued that the use of boundary objects is one of several techniques employed by scientists when working across social worlds: another is the standardization of theories and of experimental techniques. Fujimura analyzed the growth of molecular biology, observing how the research agenda was initially constructed, then scientific allies were successfully enrolled via boundary objects and standardization. She characterizes this overall process as the “scientific bandwagon,” which involves two key stages: initially progressing slowly as key actors develop and advance their ideas while negotiating meanings and alliances, then speeding up and expanding to run under its own momentum once it has gathered sufficient recognition, support, and resources. This chapter builds upon Fujimura’s notion of the scientific bandwagon in order to understand the recent and rapid rise of the OH agenda. While OH fits well with Fujimura’s description in many respects, there are some key differences, particularly around the foregrounding of interdisciplinarity, the applied nature of the agenda, and the prominence of institutional actors alongside scientific practitioners. I argue that OH shares these features with several adjacent agendas that it mutually enrolls, including food security and translational medicine, and that these may all be examples of a new style of agenda building across twenty-­first-­century science, medicine, and policy: the “interdisciplinary bandwagon.”

Methods In order to understand OH more broadly, particularly as an explicitly interdisciplinary agenda, this chapter follows OH via the usage of specific terms, in much the same way that other scholars have “followed” technologies, organisms, or

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diseases (e.g., Goedeke and Rikoon 2008; Scheffler 2014). As demonstrated in Ariane Dröscher’s (2012) study of the usage of “stem cell” in twentieth-­century biology, following terminology can be an effective approach to tracing the spread of ideas and agendas: not only is it indicative of key issues of concern, but it can also tell us about the strategic agendas, origins, and broader meanings bound up with those issues. In the increasingly interconnected domains of science/medicine/policy/industry in the early twenty-­first century, the creation and adoption of such “buzzwords” has become increasingly ubiquitous (Bensaude Vincent 2014). Therefore following the terminology of OH can be particularly productive, given that defining the meaning and origins of OH appears to be a key concern among its own advocates. This chapter draws upon search results from the citation database Web of Science, initially for the phrase “One Health,” then for a series of associated terms: “one medicine,” “one world,” AND “health,” “comparative medicine,” and “veterinary public health.” The results were cleaned to remove irrelevant references (e.g., the phrase “one health authority”) and multiple hits from conference proceedings, leaving only journal articles discussing OH. These were analyzed using bibliometrics to chart changes in usage levels over time and across fields. Alongside these indicators, the articles were also analyzed qualitatively to identify key actors, fields, terminology, and variations in the scope, aims, and meanings of OH. The qualitative analysis also drew upon the results of Google searching for these phrases, revealing the online presence of OH and the existence of a policy-­oriented “gray literature.” Several OH workshops and conferences were attended and a series of exploratory interviews with OH actors was conducted, providing background information. This enabled the development of OH to be located within a broader and longer historical context than the immediate post-­2000 visibility of the term itself. Having already introduced OH, this chapter will now outline the meanings, histories, and disciplinary origins of several terms that actors have used alongside or instead of OH to describe their aims and activities, illustrating the varying ideas that sit together under the OH umbrella. Once these have been explored, the chapter will move on to discuss the bibliometric analysis of OH terminology in journal articles, showing when and how these ideas have come together. Finally the implications of these findings for our understanding of the OH agenda and of interdisciplinary agenda building more generally will be discussed.

Veterinary Medicine and “One Medicine” As veterinary medicine emerged from human medicine in Europe during the late 1700s, doctors continued to work with animal patients until well into the nineteenth century. As such, veterinarians often sought to defend and distinguish

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their profession from its bigger and more powerful neighbor, as well as from other professions involved in the care of animals (Woods and Matthews 2010). However, vets and doctors continued to work together under the right circumstances, and the insights from such collaborations, alongside the financial and status benefits, particularly for veterinarians, provided powerful incentives to do so. Over the past two hundred years, there have been a series of veterinary agendas concerned with bringing animal and human health closer together (Bresalier et al. 2015). Comparative medicine involved the study of disease by comparing cases across a wide range of species, sometimes including humans, and was advocated and adopted by veterinary and zoological researchers from the late nineteenth century, gaining considerable traction from the 1920s onward. By the middle of the century, comparative medicine was highly influential, guiding public health research programs at the WHO for example. Comparative medicine then gradually lost prominence through the 1970s, and the term was adopted by researchers developing laboratory animal models for human clinical medical research, moving to a more anthropocentric mode of comparison (Michell 2000). Unlike comparative medicine, veterinary public health (VPH) has had a more applied orientation, involving itself with policy, regulatory structures, and public health, concerning itself with “community efforts influencing and influenced by the veterinary medical arts and sciences applied to the prevention of diseases, protection of life and promotion of the well-­being and efficiency of man” (WHO and FAO 1951, 3). VPH has particularly concerned itself with controlling disease in domestic animals in order to prevent transmission to humans via food, and maintaining animal health in order to boost food production. Like comparative medicine, VPH originated in the nineteenth century but became much more prominent from the mid-­twentieth century onward, also becoming institutionalized at the WHO at this time, but instead continuing as an active approach into the present day. “One Medicine” is often regarded as the most direct precursor to OH: the term is now generally used to refer to the alliance or cooperation of veterinary and human medical research and clinical practice, including mutual exchanges in developing new procedures, equipment, and drugs (e.g., Cardiff et al. 2008; Kaplan et al. 2009). Veterinary epidemiologist Calvin Schwabe is often credited by today’s OH advocates as the originator of the term “one medicine” in his 1984 textbook Veterinary Medicine and Human Health (Kaplan and Scott 2011; Schwabe 1984). This seven-­hundred-­page volume provided a fully articulated vision for reforming veterinary research, education, and practice, using “One Medicine” (OM) as the core organizing principle. However, searching citation databases for the term reveals that OM had been in use several decades prior to Schwabe’s book, particularly by a series of authors linked with the University of Pennsylvania (e.g., Allam 1966; Cass 1973; Schmidt 1962). During the fifties

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and sixties, medical and veterinary faculty at Penn collaborated closely and were involved in comparative medicine and VPH: today the veterinary school is a key advocate for OH (Hendricks et al. 2009). Unlike many of the terms under discussion here, OM was never defined in these texts: instead it tended to be used in a self-­evident way that implied that readers were already familiar with the term. This continued in Schwabe (1984), where OM was used in section and chapter headings: however it was not defined and first appeared in the body text as part of a historical summary. This suggests that OM was never formally “coined,” but may instead have arisen more organically in mid-­twentieth-­century thinking about animal and human health. Following Schwabe’s book (which was not highly cited until recently), OM reemerged in the early 2000s, in further discussions of comparative medicine and VPH and in pieces bringing them into alignment (e.g., Schwabe 2004). Later in the decade, key veterinary and medical associations in North America agreed on and published a series of statements and organizations promoting “One Medicine, One Health” (Kahn et al. 2008; King and One Health Initiative Task Force 2008). These events—­and the ideas behind them—­were extensively discussed in veterinary journals such as the Journal of the American Veterinary Medical Association, the U.K.-­based Veterinary Record, and Veterinaria Italiana, which published an open-­access special issue devoted to the topic.

“One World” and Its Relationship with Health The idea and term “One World” (OW) was developed by political philosophers during the Second World War, and became prominent during its aftermath, when the idea that national interests should be overcome in order to deal with international problems became highly compelling. While initially OW was articulated in the context of international relations and the formation of the UN, the idea was also instrumental in the formation of the international health agencies WHO and FAO (Brockington 1958; Staples 2011). The term OW was used by biologist Julian Huxley in his early leadership of UNESCO, and mobilized by actors in international health during postwar debates about population control and food supply (Bashford 2014; Sluga 2010). However, following this early period the term was rarely used in health contexts until it resurfaced during the 1990s, in health policy responses to the HIV/AIDS epidemic, as well as academic discussions of “emerging infectious diseases,” and the transition from “international” to “global” health (Anderson 2004; King 2004). During the 2000s these debates continued and gained an additional focus with a new series of rapidly changing viral disease threats. In 2004, following the SARS outbreak and as the H5N1 strain of avian influenza was spreading and causing widespread concern, a series of meetings themed on “One World, One

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Health” (OWOH) were organized, held initially in New York but subsequently at international locations such as China and Brazil. These meetings were organized by the U.S.-­based NGO the Wildlife Conservation Society and sponsored by the Rockefeller Foundation.4 They were specifically focused on how to manage these infections, which travel freely across countries as well as between humans, domestic animals, and wildlife, causing problems for human health, animal health, and conservation agendas. Participants included the FAO and WHO, U.S. governmental bodies including the CDC, research scientists, and a range of other conservation, disease ecology, agricultural, and public health actors (Wildlife Conservation Society 2004). Over the following years these and other international health organizations started building closer working relationships, and when the H5N1 strain of avian influenza emerged, this process accelerated, driven by the international response to the outbreak. In 2008, international agencies including FAO, WHO, OIE, and the World Bank adopted OWOH as the organizing framework for a statement of cooperative intent, fostered by this international response (FAO et al. 2008). Since then OWOH has generally been used to highlight the interconnected nature of infectious disease, as microorganisms pass between animals and humans via the wider environment. Advocates of OWOH argue that these diseases can therefore be tackled only by research and policy that encompass these domains and take a global perspective (Vallet 2009). While concerns about viral pandemics appear to have provided the primary driver for the appearance of OWOH, advocates also point to a series of disease events over the past three decades that have highlighted the animal origins of much infectious disease. These included the HIV/AIDS epidemic, the BSE/ CJD crisis in the United Kingdom, the discovery of new hemorrhagic fevers and resurgence of others, and the reemergence of older disease problems such as malaria and TB.

Transitioning to “One” Health By the mid-­2000s, both OM and OWOH were in use across research and policy in human and animal health, but as we have seen their meanings (and the actors using them) were somewhat different. While OM addressed only veterinary and human medicine, its scope included all forms of illness and clinical practice, including chronic disease and the treatment of injuries. Conversely OWOH involved a wider range of disciplines, including biological and environmental sciences, but was specifically concerned with infectious disease. The parallel statements in 2008, from the U.S. veterinary/medical associations and from the international agencies, mark a key turning point. While the statements did not cross-­refer, actors involved in both agendas advocated a move to a single banner (Zinsstag et al. 2005), and by 2010 the international health agencies (FAO et al.

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Figure 10.1. Frequency of journal articles using “One Health” and related search terms.

Source: Web of Science.

2010) had adopted OH. Figure 10.1 reflects this transition in the usage of OH and related terms in academic journal publications from 1990 until the end of 2013. It is clear that authors were starting to use OH as a standalone term from 2008, and following the FAO/WHO/OIE joint statement in 2010, OH overtook its predecessors and became adopted much more widely. Data for 2014 suggest an acceleration of this trend, with citations using the term OH nearly doubling to 173 per year, although usage of OM and OWOH persists at much lower levels. So what happened to initiate this change and the more widespread uptake of OH? Adopting OH as a single term had advantages for both OM and OWOH advocates: it was less cumbersome, significantly broadened the scope of their shared agenda, and decentered disciplines. The idea of “health” reaches far beyond infectious disease or clinical research and encompasses a much broader range of issues, practices, and policies than “medicine” can. Many advocates have embraced the flexibility of this expanded version of OH, adopting the “umbrella” metaphor as a way of articulating the inclusive nature of the agenda (One Health Sweden 2014). This shift was also driven by more pragmatic concerns: in 2008 the Wildlife Conservation Society registered the OWOH slogan as a trademark with the U.S. Patent Office, preventing its usage by other organizations. Since 2010, a biennial international conference series and journal have been founded and activities have been sponsored by research funding bodies, philanthropic foundations, and pharmaceutical companies. Moving out from

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its origins in the United States and Switzerland, OH meetings and associations have become increasingly international, appearing across Europe (e.g., Netherlands, Sweden), Southeast Asia (e.g., South Korea, Malaysia), Australia, and Africa (e.g., Ethiopia, Uganda). The ideas and terminology of OH have increasingly been used to facilitate interdepartmental cooperation in policy making and government (CDC 2013; Department of Health 2013; Leung et al. 2012). In the United Kingdom at least, several universities have merged their veterinary, medical, and biological sciences schools, referencing OH as part of the reason for these moves and launching new training programs (Royal College of Veterinary Surgeons [RCVS] 2014; University of Surrey 2012). Qualitative examination of research articles, online material, and policy reports mobilizing OH can offer further insights into the recent expansion of the agenda, how this flexibility lends itself to multiple interests, contexts, and agendas, and how different visions of interdisciplinarity are built into these texts. In biomedical, clinical, and pharmaceutical contexts, OH tends to retain the OM model of collaboration or partnership between veterinary and human medicine. A good example of this can be seen in a recent statement from the U.K. Biotechnology and Biological Sciences Research Council (BBSRC)—­a government funding body: “BBSRC will also support . . . the opportunities arising from taking a ‘One Health’ approach, in partnership with the MRC, to the support of multidisciplinary studies that underpin improvements in both human and animal health” (BBSRC 2014). While BBSRC’s central concern is with the biological sciences, the MRC concerns itself with funding and supporting medical research in the United Kingdom. OH is therefore once again being used to signal the cooperative intentions of these two organizations. Similar articulations of OH as facilitating partnerships can also be seen in commercial biomedicine, where animal health is seen as an increasingly profitable area aiding the “translation” of knowledge across the domains of pharmaceutical, agricultural, and clinical practice and research (Twine 2013). OH advocates cite “translational medicine” as an area where their approach can be of use, facilitating the movement of research insights and technical innovations between animal and human health (Immuno Valley 2014). This move develops the long-­standing role that veterinarians have played in twentieth-­century biomedical research in maintaining the health of laboratory animals, previously described as “comparative medicine” or “laboratory animal science” (Kirk 2010). Large-­scale translational research programs such as the International Knockout Mouse Project have greatly increased the numbers of animals required, and intensified demand for scientifically trained veterinarians (Davies 2012; Hendricks et al. 2009). In turn, this has reignited long-­standing debates about the balance between research and clinical practice in veterinary education (Schwabe 1984) and stimulated new training programs (Gibbs 2014). In these contexts,

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OH is invoked as a potential solution to a complex set of problems cutting across several disciplinary domains. However, just as with the earlier ideas of OM, this version of OH generally involves collaboration between well-­established disciplinary specialists, and would probably be described by scholars of interdisciplinarity as cross-­or multidisciplinary activity (Barry and Born 2013). Other U.K. funders such as the Wellcome Trust use OH primarily in connection with infectious diseases, continuing the OWOH idea that disease transmission between humans and animals can be better understood via an interdisciplinary approach (Wellcome Trust 2010). In a similar fashion to the mutual invocation of OH and translational medicine, in global health contexts agendas such as “health security” and “biosecurity” appear alongside OH around topics such as antimicrobial resistance (Department of Health 2013), influenza (Dwyer and Kirkland 2011), and Ebola (Gebreyes et al. 2014). Another key example of this can be seen in the case of “food security”—­the need to maintain an adequate food supply for human populations worldwide (FAO 1996). A long-­standing advocate of OH, the UN FAO describes OH as a “unifying force to safeguard human and animal health” (FAO 2011, 2). Just as with OH and translational medicine, this mutual deployment continues older collaborative connections: advocates of VPH played key roles in mid-­twentieth-­century WHO and FAO programs to alleviate world hunger (Bresalier et al. 2015). Beyond the FAO, many other actors in food security do not mention OH: however OH advocates often cite food security as another example of a complex, interdisciplinary, global problem that their agenda can help to address (King and One Health Initiative Task Force 2008). In a recent call for research proposals on tropical and infectious diseases, the global health funder Gates Foundation outlined their vision for OH: “If the artificial barrier that separates the fields of human and animal health could be broken down, many opportunities would emerge across the discovery-­development-­delivery spectrum for knowledge and practices in one field to accelerate progress in the other” (Gates Foundation 2013). Such a radical vision of the sciences sees disciplinary identification largely as a barrier to progressing knowledge of health and disease, and continues the OWOH tradition of bringing together a broad range of disciplines beyond veterinary and human medicine. This could be more properly described as interdisciplinarity: indeed some OH advocates argue that they are moving beyond this to a “transdisciplinary” model, also involving participatory research with local communities (Zinsstag et al. 2012).

Disciplines and Interdisciplinarity in OH While the uptake by institutions across human health, animal health, and the environment has been clear, which journals have been publishing OH articles?

Figure 10.2. Disciplinary distributions of One Health–related search terms, 1970–2012.

SOURCE: Web of Science.

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Figure 10.3. Scientific fields, actors, events, and terminologies in the recent history of One

Health.

Web of Science provides a classification of the journals covered by the database, enabling the results of keyword searches to be broken down by research field. Figure 10.2 illustrates the distribution of journal articles published using OH and associated terms. This figure immediately demonstrates that even though OH aims to bring together human and animal health, these terms are most widely used by authors publishing in animal health (veterinary sciences). These differing distributions reflect the histories traced earlier: for example, comparative medicine was widely used in veterinary journals, but also across a range of biological (parasitology, zoology) and medical (infectious diseases, public health) research fields. Similarly OWOH has been visible in veterinary science but also in fields concerned with infectious disease such as immunology and microbiology. Reflecting its history as a veterinary-led agenda, nearly 60 percent of the usage of OM has been in veterinary journals, with some mentions in medical research fields. What is most striking here is the distribution for OH itself, given that the majority of these articles were published since the consolidation of the agenda from 2008. An even higher proportion of the usage of OH has been in veterinary science journals, with some visibility in infectious disease and public health journals. There are several key inferences to be drawn from this data. First, while OH has been adopted by key policy and research institutions across multiple

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disciplines, its uptake by researchers beyond the veterinary sciences has been relatively limited. Second, the nonveterinary fields where it has been taken up are those with direct interests in key OH topics, particularly those related to infectious diseases. Finally, the differing fields allied to OM and OWOH reflect their orientations toward clinical medicine and global health/infectious diseases. Figure 10.3 provides a diagrammatic representation of the complex of alliances that have come together to form OH: something like an actor-­network diagram, but with an added dimension of change over time. It depicts relations between the key actors, scientific fields, and terminology involved in OH, the impact of disease events, and how these relations have changed over time, drawing upon the bibliometric data and historical analysis presented earlier. The upper part of the diagram shows the trajectory of OM: its roots in comparative medicine and VPH, its development by Schwabe, and the parallel growth of animal models for human biomedical research. It then shows how these came together with the additional driver of translational research into twenty-­first-­ century OM, which then merged into OH. The central area shows the trajectory of OWOH, with its origins in comparative medicine, tropical diseases, and the mid-­twentieth-­century concept of zoonosis. WHO/FAO collaborations around animal health and food supply from the 1950s onward provided a second point of interaction. HIV/AIDS created a central point of interaction between international health/development, infectious disease, and conservation actors during the 1990s. SARS and pandemic influenza concerns then played a similar role during the 2000s, leading to the emergence of OWOH, and the eventual merger into OH. Finally, the bottom of the diagram illustrates the relationship between OH and the environmental sciences. These have largely been separate, barring the interventions of the Wildlife Conservation Society, primarily concerned with the transmission of infections to and from endangered wildlife. The transition to OH has led to a much greater rhetorical emphasis on the environment, as it provides the obvious connection between humans and animals (although the data in Figure 10.2 suggest that the connection has been more rhetorical than substantive). Advocates of “EcoHealth” now position it as a successor to OH by foregrounding the importance of issues such as climate change and sustainability for health, reaching out to the environmental and social sciences (Zinsstag et al. 2012).

Discussion This chapter has so far explored the emergence of OH and situated it within a history of advocacy for the convergence of human and animal health. It has followed the appearance and usage of the term and its recent and rapid uptake by powerful actors in global health and biomedicine, alongside the key actors,

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events, disciplines, and fields involved. OH has come about through the merger of two overlapping yet distinct ideas about human and animal health: OM and OWOH. OM has its origins in twentieth-­century traditions of veterinary advocacy for closer collaboration and partnership with human medicine, including comparative medicine and VPH. In its contemporary form OM is particularly associated with clinical research, veterinarians working with laboratory animals, and translational research. OWOH originated in mid-­twentieth-­century internationalism and the founding of international health agencies such as the WHO. Twenty-­first-­century OWOH advocates have included these agencies, conservation actors, and researchers working with zoonotic disease: it is particularly associated with issues such as emerging infectious diseases and food security. OH has come about through the convergence of these interests toward the end of the 2000s, driven in particular by international responses to pandemic disease risks. Following this consolidation, OH has been adopted by a series of powerful institutional actors in global health, research funding, and policy, and the rate at which the term is used in academic journals has rapidly increased. As Leboeuf (2011) and Chien (2013) have argued, the flexibility of OH has meant the term acts as a boundary object, enabling actors across multiple social worlds (including academic disciplines) to reinterpret the agenda to suit their own interests, driving this convergence. This pattern—­of slow emergence, intense negotiation including the use of boundary objects, and consolidation, followed by widespread adoption—­fits well with Fujimura’s (1992, 1996, 1998) concept of the “scientific bandwagon,” originally applied to molecular biology. The increasing adoption of OH by institutional actors across human health, animal health, and environmental issues also suggests that it has been a highly successful bandwagon, achieving its aim of facilitating interdisciplinarity across these domains. However, the data on usage of OH terminology in academic journals indicate that discussions of OH have mostly been published in veterinary journals, appearing only in nonveterinary fields with adjacent interests (e.g., infectious diseases, public health). As we have seen, the OH agenda has its roots in long-­standing traditions of advocacy not only for veterinary-­medical partnership, but also for boosting the status and defending the boundaries of the veterinary profession, which remains small, sparsely funded, and under-­regulated in comparison to human medicine (Hobson-­West and Timmons 2015). Given this historical background of rivalry and status anxiety, combined with success in attracting the support of institutional actors including research funders, it is perhaps unsurprising that OH has faced criticism from (human) medical actors seeing it as a veterinary “land-­ grab” (Institute on Science for Global Policy 2012). This lack of movement into research practice is reflected in the current literature on OH, which tells a tale of

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anxiety and argument about how the agenda can move from “rhetoric” to “reality” (Okello et al. 2011; Gibbs 2014). The positioning of OH alongside a range of contemporary—­and usually more prominent—­agendas across science, medicine, and policy can offer further clues about OH, interdisciplinarity, and bandwagons. As we have seen, the OH literature often references terms including “food security,” “health security” “emerging infectious diseases,” and “translational medicine,” arguing that OH can provide solutions for problems in these areas. Like OH, these terms refer to new scientific and policy agendas, arguing for applied research about the world’s highly complex “wicked problems” that cut across traditional disciplinary domains (Brown et al. 2010). Like OH, these agendas provide twenty-­first-­ century articulations of long-­standing twentieth-­century concerns (e.g., “world hunger” becomes “food security”), and interdisciplinarity is seen as a key solution. Each of these addresses a set of concerns that overlap with OH, but oriented toward a different cluster of disciplines—­although OH is unique in bridging the biomedical and environmental/agricultural sciences. Rather than competing, these agendas appear to be mutually reinforcing, so arguments for OH draw upon arguments for food security or translational medicine and (at least some of the time) vice versa. The secondary literature on these agendas offers further intriguing parallels with OH: for example analyses of the discourse around food security describe it as a “master frame” (Mooney and Hunt 2009) that is deliberately broad and flexible, enabling framings and reframings by multiple actors, leading to an ultimate, if fractured, consensus (Maye and Kirwan 2013). This and similar work on global health (King 2004; Scoones and Forster 2010), translational medicine (Yaqub and Nightingale 2012), biosecurity (Dobson et al. 2013), and OH itself (Craddock and Hinchliffe 2015) argues that the dominant framings of these agendas are strategic and political, working in favor of industry and large institutional actors, often at the expense of local communities and nonprofit solutions. To return to the question of bandwagons, I argue that while OH shares many features of Fujimura’s “scientific bandwagon,” there are some key differences. In particular, OH is overtly interdisciplinary in its ambitions, extends beyond science into the policy sphere, and appears to have been constructed, aimed at, and taken up by large institutional as well as individual scientific actors. The bibliometric data suggest that the relationship between OH and scientific or clinical practice appears to be rather distanced, particularly beyond veterinary science. To put it into the terms posed by Jacobs and Frickel, OH started as a “bottom­up” movement, but as such has largely been (and remains) a deeply disciplinary concern, working to increase the status and resources of veterinarians. OH then became interdisciplinary via powerful institutional actors in biomedicine, transforming into a “top-­down” agenda, to the point that some veterinarians

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now express concerns that they will merge back into the biomedical sciences, overtaking their own interests (RCVS 2014, 10). The parallels between OH and adjacent agendas such as global health, food security, and translational medicine suggest that all of these may be examples of a new style of agenda building: the “interdisciplinary bandwagon.” Unlike the molecular biology bandwagon, which appealed to scientists in multiple disciplines by providing new techniques and ideas useful in day-­to-­day research practice, interdisciplinary bandwagons operate by providing mechanisms to facilitate institutional cooperation, gathering funding and visibility along the way (Caulfield and Condit 2012) This may explain why the language of interdisciplinarity across contemporary academia and policy tends to be so uniform and unreflective (Barry and Born 2013; Jacobs and Frickel 2009), particularly in institutional contexts: it serves to “iron out the mess of actually working together” (Donaldson et al. 2010, 1525), enabling the bandwagon building process. If this is so, then interdisciplinary bandwagons may even be an impediment to fostering practical engagement across specialisms and, as we have seen with OH, carry with them strong disciplinary interests, potentially creating resistance and resentment elsewhere. As we have seen in chapter 2 by Downey and colleagues, even the meaning of “interdisciplinarity” itself is open to multiple, sometimes conflicting interpretations that vary according to context and research field. While the impulse to elide these differences is understandable, it may contribute to misunderstandings and tensions between collaborators when they start working together on a day-­to-­day basis. Histories of interdisciplinary collaboration suggest that successful work in this mode has been driven by practical concerns such as shared research questions, exchanges of materials and methods, sociable working relationships, and supportive institutional settings (Aicardi 2014; Schlich et al. 2009), albeit still with a partial understanding of what works, when, and why. While OH discussions of the need to move “from rhetoric to reality” (Okello et al. 2011) suggest some awareness of these tensions, OH actors have rarely discussed practical steps that could be taken to facilitate collaborative research or clinical practice in particular situations. In order to move their agendas forward, advocates of OH and other interdisciplinary bandwagons would benefit by learning from how scientists have successfully managed to work together across disciplines in the past. This includes the institutional, material, political, linguistic, and financial factors contributing to an “epistemic culture” of collaboration (Smith-­Doerr et al. this volume). In other words, paying close attention to the particularly social nature of science may help to support the success of such endeavors long after the interdisciplinary bandwagon has rolled on.

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notes 1   “One Medicine? Investigating Human and Animal Disease c. 1850–­2015” is a five-­year col-

laborative project funded by Wellcome Trust (Principal Investigator Abigail Woods, grant 092719), involving a science and technology studies scholar working with historians of veterinary medicine, human medicine, and biology. I would like to thank my colleagues on the project for their support and invaluable contributions to the development of this analysis. 2   Following, e.g., Barry and Born (2013), I take “interdisciplinary” to indicate approaches that combine perspectives from multiple fields, while “cross-­disciplinary” indicates approaches where multiple fields collaborate but retain their distinct identities. It is worth noting that many scientific and policy actors, including those involved in OH, have a tendency to use terms such as inter/cross/multi/transdisciplinary almost interchangeably. 3   Adele Clarke (1991, 131) defines social worlds as “groups with shared commitments to certain activities, sharing resources of many kinds to achieve their goals, and building shared ideologies about how to go about their business.” 4   WCS, originally the New York Zoological Society, was founded in 1895, runs several wildlife parks and zoos in the United States, and undertakes international conservation research, campaigning, and activism. references

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Notes on Contributors

j i m i a d a m s is an associate professor in the Department of Health and Behavioral Sciences at the University of Colorado Denver. The focus of his research is on how networks promote or constrain the spread of things like diseases and ideas through a population. Increasingly, this work focuses on how interdisciplinary scientific fields are arranged and evolve through time. Previously, this has involved examining patterns that contribute to HIV/AIDS transmission and prevention in the United States and sub-­Saharan Africa. M at h i e u A l b e r t is associate professor in the Department of Psychiatry and the Wilson Centre for Research in Education, University of Toronto. His current work focuses on interdisciplinarity in health research. Specifically he is looking at the struggle for scientific authority between academic communities (e.g., biomedical scientists, clinical scientists, epidemiologists, social scientists) and how interdisciplinary research policies are creating new boundaries between scientific communities instead of removing them. He has published in a wide range of disciplinary and interdisciplinary journals in social science and medicine, including articles on symbolic boundaries between scientific groups (Minerva and Social Science & Medicine), science policy-­making processes (Science, Technology & Human Values), academic assessment criteria (Higher Education), and funding agencies (Canadian Journal of Higher Education). An g e l a C a ss i dy is a lecturer in the Land, Environment, Economics and Policy Institute, Department of Politics, University of Exeter. She is currently undertaking a Wellcome Research Fellowship, investigating the long-standing U.K. controversy over bovine tuberculosis and badger culling. Her research spans the contemporary history of science and medicine, communication studies, and environmental science and technology studies: she has particular interests in public scientific controversies, interdisciplinary interations, and the interactions of nature and culture. Her earlier work has investigated these issues in case studies of "One Health" (advocacy for the convergence of human and animal health, food chain risks, and public debates over popular evolutionary psychology. J e nn i f e r C r o i ss a n t is associate professor in the Department of Gender & Women’s Studies at the University of Arizona in Tucson. Besides work on the complexity of collaboration and interdisciplinarity, which is represented in this

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volume’s chapter and was supported by a grant from the National Science Foundation in collaboration with Smith-­Doerr, her current research interests include the growth of support offices for postdoctoral personnel in universities; the measurement of pain and related physiological process; and agnotology, the study of ignorance and non-­knowledge. G r e g o r y J. D o w n e y is an Evjue-­Bascom Professor in both the School of Journalism and Mass Communication and the School of Library and Information Studies at the University of Wisconsin–­Madison. He is the author of Telegraph Messenger Boys: Labor, Technology, and Geography 1850–­1950 (2002) and Closed Captioning: Subtitling, Stenography, and the Digital Convergence of Text with Television (2008), as well as co-­editor of Uncovering Labor in Information Revolutions, 1750–­2000 (with Aad Blok, 2004) and Science in Print: Essays on the History of Science and the Culture of Print (with Rima Apple and Stephen Vaughn, 2012). N o a h W e e t h F e i ns t e i n is associate professor of curriculum & instruction and community & environmental sociology at the University of Wisconsin–­ Madison. His work explores the value of science in the social and political lives of non-­scientist citizens. He is interested in identifying, understanding, and developing social mechanisms through which scientific institutions and practices can make societies more, rather than less, democratic, and believes that some of those mechanisms are educational in nature. S cot t F r i c k e l is associate professor of sociology and environment and society at Brown University, where his research and teaching interests lie primarily at the intersection of knowledge, environment, and politics. He is author of Chemical Consequences: Environmental Mutagens and the Rise of Genetic Toxicology and co-­ editor of The New Political Sociology of Science (with Kelly Moore) and Fields of Knowledge (with David J. Hess). C h i s ato F u k u d a is a PhD student in anthropology with interests in medical anthropology and science and technology studies (STS) at the University of Wisconsin–­Madison. Her dissertation research centers on efficient cookstove technologies as a means to mitigate air pollution and its associated health risks in urban shantytowns in Mongolia. A l i O. I l h a n is assistant professor of industrial design/design technology and society at Ozyegin University (OzU) in Istanbul. He holds bachelor’s and master’s degrees in industrial design from Istanbul Technical University (ITU). He completed his PhD in sociology at Washington State University (WSU) in 2013. His research interests include longitudinal/hierarchical data analysis, social

Notes on Contributors 239

network analysis, science and technology studies, sociology of education, sociology of design, and critical studies of interdisciplinarity. D a n i e l L e e K l e i n m a n is an associate dean in the Graduate School at the University of Wisconsin–­Madison, where he is also a professor in the Department of Community and Environmental Sociology. He is inaugural editor of Engaging Science, Technology, and Society, the open-­access journal of the Society for the Social Studies of Science. Among his books is Impure Cultures: University Biology and the World of Commerce (2003). Kleinman is coauthor, with Sainath Suryanarayanan, of Vanishing Bees: The Science and the Politics (2017).

is a scientist at the Wilson Centre for Research in Education, an internist at Sunnybrook Health Sciences Centre, and an assistant professor in the Department of Medicine, all at the University of Toronto. Her research program focuses on the effects of currently accepted epistemologies and knowledge production modalities within medical and medical education research on the legitimacy and/or limitations of particular subject areas within mainstream health professions education research and within health professional curricula. Her work also addresses effects of the origins and history of medical education research on that field’s definitions of legitimate knowledge production. She is involved in implementation and knowledge translation activities related to her research, particularly with respect to legitimizing non-­bioscientific knowledge within medical and other health professional curricula at multiple sites across Ontario. Ay e l e t K u p e r

E r i n L e a h e y is professor of sociology at the University of Arizona. Her scholarship focuses on scientific practice and scientific careers. She has examined the diffusion of statistical significance testing (Social Forces), data cleaning standards and lack thereof (Sociological Methods and Research), limitations to the use of mixed methods (Social Science Research), how the extent of specialization impacts academic careers and explains gender inequality therein (American Sociological Review, Gender & Society), how scientists perceive their success (Social Studies of Science), and, most recently, how interdisciplinarity shapes academics’ career outcomes. In a current (2015–­2018) grant from the National Science Foundation’s Science of Science and Innovation Policy (SciSIP) program, she is developing measures of university-­level commitment to interdisciplinary research, and examining both the precursors and the outcomes of such commitment. R ya n L i g h t is an assistant professor of sociology at the University of Oregon. He teaches and conducts research on social networks, cultural sociology, and the sociology of science.

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D av i d McB e e received a BA in sociology and a BA in philosophy from San Jose State University in 2007. He earned an MA in sociology from the University of Arizona in 2010 based upon a paper that examined the maintenance of departmental prestige in the field of sociology between 2001 and 2009. This paper contributes to the development of fuzzy-­set qualitative data analysis (fsQCA) by adapting that method to analyze longitudinal data. His current dissertation project examines the strategic use of network contacts by industrial scientists in pharmaceutical research and development teams and academic scientists in university laboratories. C y r u s C .  M . M o dy is professor and chair in the history of science, technology, and innovation at Maastricht University. He is the author of Instrumental Community: Probe Microscopy and the Path to Nanotechnology (MIT Press, 2011) and The Long Arm of Moore’s Law: Microelectronics and American Science (MIT Press, forthcoming). His chapter in this volume is part of a larger project on American physical and engineering scientists during the long 1970s. His research into the history of interdisciplinarity is informed by degrees in engineering science and science and technology studies, and by eight years teaching in the mono-­ discipline of history. H e lg a N o w ot n y is professor emerita of social studies of science, ETH Zurich, and a founding member of the European Research Council (ERC). She was vice president from 2007 onward and from 2010 until December 2013 president of the ERC. She is chair of the ERA Council Forum Austria and member of the Austrian Council for Research and Technology Development advising the Austrian government. She holds a PhD in sociology from Columbia University and a doctorate in jurisprudence from the University of Vienna. She has held teaching and research positions at King’s College, Cambridge; Ecole des Hautes Etudes en Sciences Sociales, Paris; Science Center for Social Sciences, Berlin; Collegium Budapest/Institute of Advanced Study; and others. She is Foreign Member of a number of European academies and continues to serve on many international advisory boards throughout Europe. She has received doctorates honoris causa from universities in Belgium, Germany, the Netherlands, Norway, and the United Kingdom and from the Weizmann Institute of Science, Israel. She has published widely in social studies of science and technology and on social time. Her new book, The Cunning of Uncertainty, was released in 2015. A a r o n Pa n o fs k y is an associate professor in public policy, the Institute for Society and Genetics, and sociology at UCLA. His book, Misbehaving Science: Controversy and the Development of Behavior Genetics, is a historical analysis of how coping with a series of controversies has limited scientists’ efforts to understand the inheritance of behavior. He has also studied public participation

Notes on Contributors 241

in science through Internet-­mediated platforms and patient advocacy in rare genetics research. His current research looks at race and genetics from various perspectives: the ambiguities of the race concept in genetics, how behavior geneticists manage race researchers in their midst, and how racists understand race research. He is also beginning a new project on the perception and management of “reproducibility crisis” among other public problems across fields in contemporary science. E l i s e Pa r a d i s is assistant professor at the University of Toronto in the Leslie Dan Faculty of Pharmacy and in the Faculty of Medicine’s Department of Anesthesia. She studies collaborative health care practices and health professions education. Her research—­inspired by sociological theory on the professions, Pierre Bourdieu’s theory of practice, and neo-­institutional theory—­aims to transform how teams work together. Her research has been published in a wide range of clinical, educational, and sociological journals, including the Journal of Critical Care, Medical Education, Social Science & Medicine, and Body & Society, with forthcoming publications in Health Services Research and Advances in Health Sciences Education.

has an MA in human geography from the University of Wisconsin–­Madison, focusing on feminist and economic geographies of the United States and science and technology studies (STS). She is pursuing a second advanced degree in journalism and mass communication at the University of Wisconsin–­Madison, exploring ways to translate the insights from scholarly research through various media (i.e., long-­form nonfiction essays, documentary photography, audio storytelling, and comic art) to broader publics. Sigrid Peterson

B a r b a r a P r a i ns a c k is a professor in the Department of Social Science, Health & Medicine at King’s College London. Her work explores the social, ethical, and regulatory dimensions of genomic research, technology, and medicine. Her current projects focus on participatory practices in medicine, on the notion of personalization, and on the role of solidarity in guiding practice and policy in this field. Her latest book is Solidarity in Biomedicine and Beyond (with Alena Buyx, 2016).

is a lecturer in sociology in the department of Social Sciences, Media and Communications at Brunel University London. With a PhD in sociology of science from UCL, he has previously worked on the public understanding of uncertainty, risk, and energy policy at the University of Cambridge and on scientists’ perspectives of working in citizen science at the OPAL project at Imperial College London. Next to citizen science, his current research interests include science in popular culture, science comedy, and interdisciplinary relationships between philosophy, history, and sociology of science. H au k e R i e sch

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is a PhD student in sociology at the University of Massachusetts, Amherst. His research looks at the effects of commercialization on academic science, specifically focusing on the organization of graduate student education, collaborative research efforts, and university-­ industry relations. Currently, he is collecting data for his dissertation, a comparative ethnography examining two organizations. The first is an interdisciplinary research center that creates novel products for the market, while the second is an NSF-­funded traineeship program. He is particularly interested in the different ways students are educated for careers in science in each organization, and how this can further theory on how commercial forces shape the cultural, political, and economic landscape of academic science. T i m ot h y S a cco

L au r e l S m i t h -­D o e r r is professor of sociology and director of the Institute for Social Science Research (https://www.umass.edu/issr/) at the University of Massachusetts, Amherst. Her recent research focuses on collaboration among natural scientists, and gender equity in scientific contexts; she is the PI on two U.S. National Science Foundation grants on these topics, including the collaborative grant with Croissant informing the chapter in this volume. She is also a member of the UMass leadership team for an NSF NRT grant on Soft Materials for Life Sciences, bringing expertise with interdisciplinary collaboration and science policy to the project. She was a visiting scientist at NSF (2007–­2009) and there received the NSF Director’s Award for Collaborative Integration. She is an editor of the forthcoming new edition of the Handbook of Science and Technology Studies, commissioned by the Society for Social Studies of Science (4S).

is a lecturer in sociology at Boston University. His research focuses on technological cultures, knowledge regimes, and social problems. He is working on a book project that investigates the public discourse on traffic accidents in the United States. I ta i Va r d i

Index

Note that page numbers in italics represent figures and tables. Abbot, Andrew, 151–­154, 157, 160, 162, 165 academic science, 71, 76, 80 academic value and interdisciplinarity, 6, 128–­147 accounting approach to evaluation, 92–­95 ACS. See American Chemical Society AGP. See American Gut Project Albert, Mathieu, 31, 69–­70 ambiguity, 65–­103, 132 American Chemical Society (ACS), 71 American Gut Project (AGP), 204, 209nn1, 6 American Optical, 183 American Sociological Review, 29 American studies, 133–­134 Angell, Jim, 186 animal behavior genetics, 113–­114, 115, 116, 118 animal health, 213–­235 architecture, 51–­52, 55–­62 ARPA. See U.S. Advanced Research Projects Agency Audubon Society, 198–­199 autonomy, maintaining, 78 Avantek, 183 bachelor’s degree, 149, 151–­157, 155, 156, 158, 159, 162–­164, 163 bandwagon, 216, 228–­230 basic science, 142 BBSRC. See Biotechnology and Biological Science Research Council Beckman, Christine, 27 behavior genetics, 15, 17: animal behavior geneticists, 113–­116, 118; competing visions, 113–­116; knowledge production, 107–­108, 116–­122; molecular geneticists, 113, 114, 117; origins and fragmentation,

110–­113; psychiatric geneticists, 113, 114, 118; psychologists, 113, 114–­116, 118 Behavior Genetics, 111, 115, 120 Behavior Genetics Association, 111, 115 Bell Labs, 175, 182 bench work/science, 73, 142–­143 BGP. See British Gut Project bibliometrics, 14, 139, 166, 217, 227, 229 “biohacking,” 209n4 biomedicine, doxa of, 100 Biotechnology and Biological Science Research Council (BBSRC; UK), 222 biotechnology cluster, 174 Birnbaum, Philip, 29 Bliss, James, 180, 181 Boix Mansilla, Veronica, 69 Bonney, Rick 197 book publishing, 94–­95 boundary object/boundary work, 12–­17, 127–­138, 130, 143–­144, 215–­216, 228 Bourdieu, Pierre, 87–­88, 108–­109, 112–­113, 116, 123 Brint, Steven, 152 British Gut Project (BGP), 203–­205, 207, 209nn1, 6 Bush, George H. W., 190 calibration, 69 Canadian Institutes of Health Research (CIHR), 14–­15, 31, 86–­87: Medical Research Council, 86; Social Sciences and Humanities Research Council, 87, 93 Carnegie Foundation, 154 Carter, Jimmy, 190 CCRMA. See Stanford University, Center for Computer Research in Music and Acoustics CDC. See U.S. Centers for Disease Control and Prevention Centellas, Kate M., 69

243

244 index Chaos of Disciplines (Abbott), 151 Charney, Evan, 117 chemical sciences, 65–­69, 71–­83 Chien, Yu-­Ju, 215–­216, 228 Chowning, John, 186–­187 CIHR. See Canadian Institutes of Health Research CIS. See Stanford University, Center for Interdisciplinary Research citizen science (CS), 16, 18, 209n1: categories of, 197–­199; described, 194–­197, 205–­208; effects of, 199–­205; history, 197–­199; movement, 2, 13; Open Air Laboratories (OPAL), 202–­203 climate change, 227 clinical research, 86–­103, 142–­143 cluster hire program, 52 Coburn, G. C., 183 cognitive contextualization, 85, 91, 99–­100 cognitive resources, 29, 34–­36, 43 Cohen-­Cole, Jamie, 184 coherence, 65–­103 Cold War projects, 174–­176, 182, 190 collaboration: credit in, 75–­77, 80; defining, 66–­69; instrumental collaboration, 67; “only connect,” 2; research on, 69–­70; “team science,” 6; tension, 75, 79 communication, 6, 15, 29, 36, 68. See also language community, 142, 142, 145n2 comparative design/study, 11, 74 comparative medicine, 217–­219, 222, 226–­228 Cornell University, 189: Laboratory of Ornithology, 197 criminology, 149–­169, 167n10 crossdisciplinarity research, 3, 68–­70, 215, 223, 231n2 crowdsourcing, 195, 198 CS. See citizen science CV elements, 90 decoupling, 30, 40, 43, 81, 87–­88, 100 demography, 128, 134, 136–­138, 144, 145n4 Dickens, William T., 119 “Digital Systems for the Generation of Musical Sound,” 186 disciplinary domains: -­based education, 152; constraints, 7, 13–­15; defined,

145n1; described, 107–­108; differences between interdisciplinary knowledge, 12, 30; framing, 41; knowledge project, 129; limitations, 7; longitudinal comparison, 148–­169; One Health, 215, 223, 224–­225, 226–­227, 229; secondary, 28; silo, 7, 13–­ 15, 16, 57, 107, 196, 215; specialization, 127, 128, 131 disclosure concerns, 73 DIY. See do-­it-­yourself Dobzhansky, Theodosius, 110 doctorate degree, 151–­153 do-­it-­yourself (DIY), 200, 209n4 domain-­spanning. See disciplinary domains doxa, 88–­92, 95, 98, 100 Dröscher, Ariane, 217 Earnest, Les, 186 Earth Microbiome Project, 209n6 EcoHealth, 227 Einstein School of Medicine, Albert, 182 electronics, 181–­184, 187, 189–­190 emerging infectious disease. See infectious disease Emory University, 27 English, William, 117 environment, 213–­235 epistemic cultures, 9–­10, 15, 31: ambiguity in academic practices, 74, 76–­77; ambiguity in industry practices, 74, 77–­80; ambiguity in narratives, 74, 75–­76; coherence and ambiguity, 65–­66; coherence in narratives, 74–­75; collaboration and —­, 66–­69, 71–­72; data and methods, 65, 72–­74; described, 65, 71; differences, 28–­29, 36, 41; organizational contexts, 70–­71; research on collaboration, 69–­70 Epstein, Steven, 198 European Commission: Horizon2020, 208 evaluation, 1, 29–­30 43, 85–­86, 88, 90–­95, 98–­100 expansion (boundary work), 129 expertise, 34–­35, 44 expulsion (boundary work), 129 Eysenck, Hans, 121 fact-­checking, 39 faculty: administrative support, 30–­31, 40, 43; career trajectory, 91, 97; CV elements,

90; diversity, 148; interpersonal support, 40, 43; medical, 84–­103; organization, 148–­151, 151; professional associations, 44; promotion, 30 43; risks/challenges, 28–46; study of, 27 Fairchild Semiconductor, 183 FAO. See Food and Agriculture Organization field presence, 157, 160–­162, 161 Fifield, Steve, 69 Fisher-­Pearson statistical refinement, 110 Flynn, James R., 119 Flynn effect, 119 Food and Agriculture Organization (FAO), 214, 215, 219, 220, 223, 227 food security, 214, 216, 223, 227–­230 Ford Foundation, 150, 179 framing/audience, 29–­30, 37, 39, 41–­42 Franklin, Benjamin, 198 Freeman, Linton C., 145n2 Frickel, Scott, 28, 70, 123–­124, 145n1, 215, 229 Frodeman, Robert, 132 Frost, Susan H., 27 Fujimura, Joan, 216, 228, 229 funding: accounting evaluation, 93; agency, 2–­3, 27; availability, 29, 40, 43, 96, 127; coherence and ambiguity, 80–­81; citizen science, 204–­205, 207; hybrid financial structure, 49, 50–­51; mechanisms, 5; One Health, 213, 221–­222, 228–­230; Stanford University, 173–­175, 181–­190; Wisconsin Institutes for Discovery, 47–­49, 52–­53, 62–­63. See also interdisciplinarity research, support Galton, Francis, 110 “garage biology,” 209n4 Gates Foundation, 214, 223 Geballe, Ted, 183 genetic heritability, 116–­121, 124n2 genetic toxicology, 123–­124 geography, 149–­169, 167n10 Georgia Tech, Nanotechnology Research Center, 190 Goffman, Erving, 73 Google, 217 Gottfredson, Lynda, 121 graduate students, 59, 96 grants, 29, 91

Index 245 Griesemer, James, 215 Griswold, Wendy, 72, 80 Guetzkow, Joshua, 29, 85 Hackett, Edward J., 59, 70 hard science. See science Hartford Foundation, John A., 182 Harvard Educational Review, 111 health security, 229 HEGIS. See Higher Education General Information Survey Hernstein, Richard, 121 Hess, David, 95 Hewlett-­Packard, 183 hierarchies and constraints, 7, 15–­16 Higher Education General Information Survey (HEGIS), 153–­154, 164–­165 high-­impact journal, 91 HIV/AIDS research, 134, 138–­143, 144, 219–­220, 227: prevent mother-­to-­child transmission (PMTCT), 139–­141, 140, 144, 145n6 Hodges, Brian D., 69–­70 “How Much Can We Boost IQ and Scholastic Achievement” ( Jensen), 111 Human Genome Project, 117 human health, 213–­235 humanities, 4, 8, 17, 27, 29, 84–­103 humor, 76–­79, 80 Huxley, Julian, 219 IBM, 183, 190 industry science, 71, 73, 80 infectious disease, 227–­229 “Innovative Measurement Technology for the Semiconductor Industry,” 184 Integrated Postsecondary Education Data Survey (IPEDS), 153–­154, 164–­166 Intel Corporation, 186 intellectual property, 73 intelligence and race. See racial research interdisciplinarity research (IRD): academic value, 6; administrative overhead, 60; approaches, 127; as a social struggle 113–­ 116; assessment, 96–­99; assumptions, 10–­16; behavior genetics case, 107–­126; benefits, 27; coherence and ambiguity, 65–­ 103; commercial focus, 69–­70; commitment, 37–­38, 43; concept, 1; consequence

246 index interdisciplinarity research (IRD) (continued) interdisciplinarity research of disciplinary structure, 152; coordination challenges, 28–­29; costs, 27, 28; defined, 12, 145n1, 231n2; described, 107–­108, 205–­208; differences between disciplinary knowledge, 12; difficulties, 28–­34, 34–­36; discipline constraints, 13–­15; economic interest, 5; evaluating, 1, 29–­30 43; fantasy, 84–­103; framing, 41–­42; frictions of, 47–­64; government innovation, 5; hierarchies and constraints, 15–­16; initiative, 27; knowledge project, 129; limiting factors, 13; literature, 6, 8–­10, 84, 107; longitudinal comparison, 148–­169; meaning, 230; One Health, 215, 231n2, 223, 226–­227; one medicine, 213–­235; organizational context, 70–­71; partnerships, 174–­175; political interest, 5; power relationships, 86; prioritization, 131; problem-­oriented research, 175–­177; productivity, 39 43; socio-­emotional-­cognitive platforms, 31; support, 30–­31, 40–­41; temporal dimension, 127–­128; term described, 48; time constraints, 38–­39, 43, 80; title search, 27–­46; types of, 3, 27, 70, 84, 128, 131; Vietnam-­era Stanford, 173–­193. See also faculty interdisciplinarity research center, 27 international health organizations. See specific organization International Knockout Mouse Project, 222 international relations, 149–­169, 166n3, 167n10 “invisible college,” 135, 137 IPEDS. See Integrated Postsecondary Education Data Survey IQ controversy. See racial research IRD. See interdisciplinarity research Irwin, Alan, 196–­198 Jacobs, Jerry A., 28, 108, 130, 132, 133, 145n1, 165, 215, 229 Jean, Paul M., 27 Jensen, Arthur, 111–­112, 116, 121 journal: high-­impact, 91; term frequency, 221, 223, 224–­225, 226–­228. See also publication

Journal of the American Veterinary Medical Association, 219 Kaggle (crowdsourcing platform), 198 Kamin, Leon, 112 Kerr, Clark, 157 King’s College London: Citizen Participation in Science and Medicine Network, 200, 203–­204 Kline, Stephen, 176, 185 Knorr Cetina, Karin, 65, 70, 71 knowledge production, 50, 71: boundaries within, 129; categorization, 12; discipline constraints, 13–­15; ecologies of interdisciplinary —­, 8–­9; gaps, 9–­10; group/individual motivation, 130–­ 134; integrating, 36; interdisciplinary knowledge as better knowledge, 7, 11–­12, 16; life course, 128, 131–­147; limits/constraints, 110; multidimensional approach, 127–­147; phases and aspects, 9; repetitive, 15, 107–­108, 116–­122; sharing, 44; trajectory, 133 Kompfner, Rudi, 175, 179–­182 lab-­based research model, 95–­96 Laberge, Suzanne, 69–­70 laboratory animal science, 222 Lamont, Michele, 29, 69, 85, 100 language, 1–­2, 6, 129, 216–­217. See also communication Lattuca, Lisa, 27 43 Leahey, Erin, 27 Leboeuf, Aline, 228 Lewontin, Richard, 112 Light, Robert, 135 Lingard, Lorelei, 69–­70 Linvill, Candace, 180 Linvill, John, 180–­181, 182, 189 Lyman, Richard, 121, 177–­179, 187 Mallard, Gregoire, 29, 85 Marx, Karl, 87 May, Matthew, 136 Mayer, Karl Ulrich, 131, 132 Mayo Clinic, 182 medical school, study of, 27 medicine, faculties of, 84–­103 Meindl, James, 180, 181, 189–­190

Mellon Foundation, Andrew W.: New Directions Fellowships, 31–­42 Merton, Robert K., 71 metaphor, 48, 50–­51, 53, 56–­57, 62, 129, 221 military, 174–­177, 180–­182, 184 military industrial complex, 174 Miller, William F., 176 MIR. See Morgridge Institute for Research mistakes, managing, 79–­80 molecular genetics, 113, 114, 117 Montpellier, France, 183 Moody, James, 135 Moore, Dick, 186 Moore, Kelly, 70 Moore, Rob, 14 Morgridge Institute for Research (MIR), 49, 52–­62 motivation, 130–­134 multidisciplinarity research, 3, 70, 84, 128–­ 145, 145n1, 223 Murphy, Scott P., 152 Murray, Charles, 121 NASA, 174, 180, 186 National Cancer Institute, 183 National Endowment for the Arts, 186 National Institute of Mental Health, 186 National Institutes of Health (NIH), 181, 183, 187, 190 National Nanofabrication Users Network, 189–­190 National Nanotechnology Infrastructure Network, 190 National Science Foundation, 183, 186, 189, 190: Research Applied to National Needs program, 181; Research Development Fund, 186 Nazism, 110 network, 43, 129, 135, 137, 139–­141, 145nn2, 6, 166 nevirapine, 140, 144 New Directions Fellowships, 31–­42 NIH. See National Institutes of Health Nixon, Richard, 183 OH. See One Health movement OIE. See World Organization for Animal Health OM. See One Medicine

Index 247 One Health Initiative, 213 One Health (OH) movement, 13, 213–­217, 227–­230: disciplines and interdisciplinarity, 223, 226–­227; relationship with health, 219–­220; transitioning to, 220–­223; veterinary medicine, 217–­219 One Medicine (OM), 218–­219, 221, 226–­228 “One World” (OW), 219–­220 One World, One Health (OWOH), 219–­ 221, 223, 226–­228 “only connect” collaboration, 2 OPAL. See Open Air Laboratories Open Access, 2 Open Air Laboratories (OPAL), 198, 202–­ 203, 207 Optacon (OPtical to TActile CONverter), 180–­181, 189 OPtical to TActile CONverter. See Optacon organizational context, 70–­71, 80–­81, 128, 131 organization ecology, 28–­34, 48–­49. See also decoupling OW. See “One World” OWOH. See One World, One Health Packer, Herbert L., 179 pandemic influenza, 227–­228 Paradis, Elise, 31 Parker, John N., 70 PatientsLikeMe platform, 209n2 peer review, 1, 70, 99 Pentagon, 186, 188 Pitzer, Kenneth, 174, 178 Plummer, Jim, 189 PMTCT. See HIV/AIDS research political science, 149–­169, 167n10 Poole, David, 186 Porter, Allan L., 27 postdoctoral fellows, 59 power, 40, 44, 69, 85–­86, 95–­100, 123, 128, 179 prevent mother-­to-­child transmission. See HIV/AIDS research Proctor, Kristopher, 152 productivity, 28, 30, 32, 39, 43, 86, 88, 90–­95, 98–­99, 116 professional support, 31, 70 protests. See Vietnam-­era protests

248 index psychiatric genetics, 113, 114, 118 psychologists, 113, 114–­116, 118 publication: book, 94–­95; citation, 3; collaboration, 75; expectations, 91–­92; first-­author paper, 67; high-­impact journal, 91; journal types, 93; multiple-­authored, 3; review process, 29–­30, 41–­42; sole-­ authored, 28–­29. See also journal public engagement. See citizen science Public Understanding of Science (PUS), 196 PubMed, 95 PUS. See Public Understanding of Science qualitative methods, 27, 70, 73–­74, 97–­98, 217, 222 Quate, Calvin, 181–­184, 187, 190 racial research, 108, 109, 110–­112, 113, 120, 121 Rafols, Ismael, 27 Rambo, William, 187–­188 RDF. See National Science Foundation, Research Development Fund Reagan, Ronald, 190 real-­world solutions, 1–­4 Regehr, Glenn, 69–­70 religion, 128, 134–­136, 143–­144, 145n3 research impact, 208 resource sharing, 68, 127 Responsible Research and Innovation, 2, 208 Rhoten, Diana R., 59 Rockefeller Foundation, 150, 220 “routine interdisciplinarity,” 14 Royal Society for the Protection of Birds, 199 Rushton, J. Philippe, 121 SAIL. See Stanford University, Artificial Intelligence Laboratory SARS, 227 Sato, Kyoko, 69 SCEI. See socio-­emotional-­cognitive platforms Schwabe, Calvin, 218–­219, 227 science, 2, 3, 4, 28. See also publication scientific capital, 108–­109, 115, 118, 120–­124 SDS. See Students for a Democratic Society serendipity, role in research, 3 Silicon Valley, 174

silo. See disciplinary domains/ domain-­spanning Silvertown, Jonathan, 198 Simon, Rita, 29 skepticism, 187–­188 Smardon, Regina E., 69 Smilde, David, 136 social science, 4, 11–­12, 17–­18, 27, 28, 84–­ 103, 148–­169 social sciences and humanities (SSH), 85–­103 social sciences of religion. See religion Social Sciences Research Council, 150 social theory, 4 social value and interdisciplinarity, 6 Society for the Scientific Study of Religion, 134 socio-­emotional-­cognitive platforms (SCEI), 31, 34, 44 sociology, 149–­169, 167 n10 Spector, Tim, 204 SRI. See Stanford University, Research Institute SSH. See social sciences and humanities Stanford University, 173–­175: active to passive interdisciplinarity, 189–­190; Applied Electronics Laboratory, 174, 177; Artificial Intelligence Laboratory (SAIL), 185–­186; Artificial Intelligence Project, 186; Biophysics Lab, 182; Center for Computer Research in Music and Acoustics (CCRMA), 186; Center for Interdisciplinary Research, 179, 185, 189; degree-­granting programs, 184, 185; divestment, 176–­177; entrepreneurial university, 174; Institute for Public Policy Analysis, 185; Integrated Circuits Laboratory, 180, 181, 189; interdisciplinarity and problem-­oriented research, 175–­177; Microwave Laboratory , 182; motivations for interdisciplinarity research, 177–­180; new institutions, 184–­187; new research directions, 181–­184; Research Institute, 174, 176–­177; Science, Technology, and Society, 185; skepticism and disappointment, 187–­188; Synchrotron Radiation Project, 189; Values, Technology, and Society program, 176, 185, 188; visibility, 180–­181

Stanko, Taryn, 27 Star, Susan Leigh, 215, 216 stem cell research 48–­49, 55, 217 Stoolmiller, Mike, 119 Strober, Myra H., 43 Students for a Democratic Society (SDS), 178, 188 Study of Education at Stanford, 178 subdiscipline, 67–­68, 71 “team science,” 6 Telesensory Systems, 181 Tempini, Niccolo, 209n2 temporal dimension, 127–­128 Terman, Fred, 174–­175, 181–­182 theology. See religion thought collective, 135. See also “invisible college” “trading zones,” 43 transdisciplinarity research, 3, 70, 84, 128, 129, 131, 145n1 translational medicine, 216, 222–­223, 227–­230 Turk-­Bicakci, Lori, 152 Turner, Stephen, 109, 150 University of California, Irvine, 182 University of California, San Diego, 186, 204–­205 University of Colorado, 204 University of North Dakota, 182 University of Pennsylvania, 218–­219 University of Wisconsin–­Madison. See Wisconsin Institute for Discovery urban studies, 149–­169, 166n2, 167n10 U.S. Advanced Research Projects Agency (ARPA), 184, 186 U.S. Air Force, 180 U.S. Atomic Energy Commission, 174 U.S. Bureau of Labor Statistics, 154, 157 U.S. Census Bureau, 154, 157 U.S. Centers for Disease Control and Prevention (CDC), 214, 220 U.S. Department of Agriculture, 183 U.S. Joint Services Electronics Program, 181 U.S. National Bureau of Standards, 184

Index 249 U.S. Office of Education, 181 U.S. Office of Naval Research, 180, 182, 184 vaccine development, 142–­143 Veterinaria Italiana, 219 veterinary medicine, 213–­214, 217–­219 Veterinary Medicine and Human Health (Schwabe), 218 veterinary public health (VPH), 218–­219, 223, 227–­228 Veterinary Record, 219 Vietnam-­era protests, 173–­193 Vincenti, Walter, 185 VPH. See veterinary public health WARF. See Wisconsin Alumni Research Foundation WCS. See Wildlife Conservation Society Web of Science, 217, 226 Wellcome Trust, 214, 223 Whitney, Glayde, 121 WHO. See World Health Organization WID. See Wisconsin Institute for Discovery Wildlife Conservation Society (WCS), 220–­ 221, 227, 231n4 Wisconsin Alumni Research Foundation (WARF), 49, 53 Wisconsin Institute for Discovery (WID): “Cluster Hire” program, 52; described, 47–­51; design process, 51–­57; Discovery Building, 47–­48; Discovery Institutes, 48–­49; faculty, 59–­61; frictions of interdisciplinarity, 57–­61, 62; funding, 49, 50–­51, 53, 62; hybrid structures, 48–­49, 62; innovations, 48–­49; physical design, 57–­60; social metaphors, 52–­57, 58–­59 World Bank, 220 World Health Organization (WHO), 214, 215, 218, 219, 220, 227–­228 World Organization for Animal Health (OIE), 214, 215, 219, 220 Wynne, Brian, 196 Yamaha, 187