Data and Doctor Doom: An Empirical Approach To Transmedia Characters (Palgrave Studies in Comics and Graphic Novels) 3031451724, 9783031451720

Table of contents :
Acknowledgements
Contents
List of Figures
List of Tables
1 Introduction
References
2 Methodology
Comics Studies
Interdisciplinary Research
Empirical Research
Grounded Theory
Existing Models of Transmedia Characters
Transmedia Storytelling
Transmedia Archaeology and Early Transmedia Characters
Characters Carrying the Storyworld
Global Transmedia Character Networks
Character Signifiers
Storyworld Components
Story/Worlds/Media
Existential and Fictional Identity
Behaviour
The Psycholexical Approach
Personality Traits and Characters
Motivation
Behaviour Summary
Character-Building, World-Building and Authorship
A Unified Catalogue of Transmedia Character Components
Character-Specific Components
Appearance
Names and Titles
Physical Actions
Dialogue
Storyworld-Specific Components
Locations
Other Characters
Objects
Previous Events
Behavioural Components
Perceived Behaviour
Personality Traits
Motivations
Authorship Components
Market Authors
Textual Authors
Summary
References
3 The Corpus and Sample
Choosing Doom
Inclusion and Exclusion Criteria
Inclusion Criteria
Exclusion Criteria
Identifying the Corpus
Sources for Comics Texts
Other Types of Text
Selecting a Sample
Sample Significance and Representativeness
Sampling Methodology
The Signifier Survey
Survey Design
Setting Up the Online Survey
Finding an Audience
Cleaning the Data
Signifier Survey Analysis
Your Experience of Doctor Doom
About Doctor Doom
Doctor Doom’s World
Creators and Marketing
Anything Else
Signifier Survey Conclusion
Effectiveness of the Survey
Overall Results
Using the Results to Generate a List of Signifiers
Appearance
Names and Titles
Physical Actions
Behaviour
Dialogue
Other Characters
Objects
Locations
Previous Events
Textual Authors
Market Authors
References
4 Analysis
Collecting the Data
Database Design
Data Entry
Appearance
Names and Titles
Physical Actions
Dialogue
Locations
Other Characters
Objects
Previous Events
Perceived Behaviour
Personality Traits
Motivation
Market Authors
Textual Authors
Data Entry Conclusion
Analysis of Character-Specific Components
Appearance
Names and Titles
Physical Actions
Dialogue
Summary of Character-Specific Components
Analysis of Storyworld-Specific Components
Other Characters
Objects
Settings
Previous Events
Summary of Storyworld-Specific Components
Analysis of Behavioural Components
Perceived Behaviour
Behaviour—BFI
Motivations
Summary of Behavioural Components
Analysis of Authorship Components
Market Authors
Textual Authors
Summary of Authorship Components
Conclusion
References
5 A Tale Of Two Menaces
The Sample
Data Entry
Character-Specific Components
Appearance
Names and Titles
Physical Actions
Dialogue
Character-Specific Summary
Storyworld-Specific Components
Other Characters
Objects
Settings
Previous Events
Storyworld-Specific Summary
Behavioural Components
Perceived Behaviour
BFI
Motivation
Behavioural Summary
Authorial Components
Market Authors
Textual Authors
Authorial Summary
Overall Analysis
References
6 Discussion
The Corpus and Sample
Data Design and Data Entry
Data Analysis
The Signifier Survey
Comparison with Main Analysis
Conclusion
Does the Catalogue Work?
Using Doctor Doom
Donald Goes Dutch
Other Possibilities
References
Appendix A: Using the Unified Model of Transmedia Character Coherence
Preparation
Data Collection
Character-Specific Components
Storyworld-Specific Components
Behavioural Components
Authorship Components
Analysis
Appendix B: Doctor Doom Corpus
Appendix C: Example of Signifier Survey
[Character Name] SURVEY
1. Your Experience of [Character Name]
Media Types
Comics
Comics Series
Movies
Animated TV Shows
Video Games
2. About [Character Name]
Appearance
Names and Titles
Physical Actions
Dialogue
3. [Character Name]’s World
Other Characters
Objects
Locations
Previous Events
4. [Character Name]’s Behaviour
Described Behaviour
Motivations
Personality Traits
5. Creators and Marketing
Creators
Marketing
6. Other Associations
Bibliography
Index

Citation preview

PALGRAVE STUDIES IN COMICS AND GRAPHIC NOVELS

DATA AND DOCTOR DOOM An Empirical Approach To Transmedia Characters

Mark Hibbett

Palgrave Studies in Comics and Graphic Novels

Series Editor Roger Sabin, University of the Arts London, London, UK

This series concerns Comics Studies—with a capital “c” and a capital “s.” It feels good to write it that way. From emerging as a fringe interest within Literature and Media/Cultural Studies departments, to becoming a minor field, to maturing into the fastest growing field in the Humanities, to becoming a nascent discipline, the journey has been a hard but spectacular one. Those capital letters have been earned. Palgrave Studies in Comics and Graphic Novels covers all aspects of the comic strip, comic book, and graphic novel, explored through clear and informative texts offering expansive coverage and theoretical sophistication. It is international in scope and provides a space in which scholars from all backgrounds can present new thinking about politics, history, aesthetics, production, distribution, and reception as well as the digital realm. Books appear in one of two forms: traditional monographs of 60,000 to 90,000 words and shorter works (Palgrave Pivots) of 20,000 to 50,000 words. All are rigorously peer-reviewed. Palgrave Pivots include new takes on theory, concise histories, and—not least—considered provocations. After all, Comics Studies may have come a long way, but it can’t progress without a little prodding. Series Editor Roger Sabin is Professor of Popular Culture at the University of the Arts London, UK. His books include Adult Comics: An Introduction and Comics, Comix and Graphic Novels, and he is part of the team that put together the Marie Duval Archive. He serves on the boards of key academic journals in the field, reviews graphic novels for international media, and consults on comics-related projects for the BBC, Channel 4, Tate Gallery, The British Museum and The British Library. The ‘Sabin Award’ is given annually at the International Graphic Novels and Comics Conference.

Mark Hibbett

Data and Doctor Doom An Empirical Approach To Transmedia Characters

Mark Hibbett University of the Arts London London, UK

ISSN 2634-6370 ISSN 2634-6389 (electronic) Palgrave Studies in Comics and Graphic Novels ISBN 978-3-031-45172-0 ISBN 978-3-031-45173-7 (eBook) https://doi.org/10.1007/978-3-031-45173-7 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Cover credit: Mark Hibbett This Palgrave Macmillan imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland Paper in this product is recyclable.

Acknowledgements

Thanks to the dream team of Roger Sabin and Ian Horton for their ongoing support throughout the whole process of creating this book, which they have guided with enthusiasm, advice, and jokes. I’m similarly grateful to the Comics Research Hub at UAL, notably my two Ph.D. siblings Guy Lawley and Tobias Yu-Kiener. A major turning point in my thinking about the topics discussed came when I attended the Tubingen Winter School in 2018. Many thanks to the organisers and other attendees for making me feel so welcome and giving me so much to think about. Thanks to the members of The Montgolfier Group, the COMIXSCHOLARS-L mailing list and the Facebook groups Comic Book Historians and London Loves Comics, who were always happy to answer my questions. Thanks to Paul Lambert, Peter Buckley Hill and Neil Brown for their help with statistical questions, to Michael Larkin for his advice about the Big Five Index, and to Noah Hibbett for his thoughts on Doctor Doom’s costume. Also thanks to everyone who promoted the signifier survey online (especially Ryan North) and those who took the time to answer it. Thanks to my former boss Ted Melhuish, whose advice to ‘eyeball the data’ has rung in my head throughout this process. Thanks to Steven Carter, Pradeep Bali, Sanjiv Bali and Paul Myland for being there when all this started, and to the late Pete Wells at The House On The Borderland for accepting our dinner money in order to facilitate it. Finally, thanks above all to Charlotte Wadsworth for suggesting I do this in the first place, and for all the ideas, suggestions, and discussions since. Thanks!

v

Contents

1

Introduction References

2

Methodology Comics Studies Interdisciplinary Research Empirical Research Grounded Theory Existing Models of Transmedia Characters Transmedia Storytelling Transmedia Archaeology and Early Transmedia Characters Characters Carrying the Storyworld Global Transmedia Character Networks Character Signifiers Storyworld Components Story/Worlds/Media Existential and Fictional Identity Behaviour The Psycholexical Approach Personality Traits and Characters Motivation Behaviour Summary Character-Building, World-Building and Authorship A Unified Catalogue of Transmedia Character Components Character-Specific Components Appearance Names and Titles Physical Actions Dialogue Storyworld-Specific Components

1 5 9 9 10 10 12 13 13 15 17 18 19 21 22 24 26 27 28 30 32 33 34 35 35 35 35 35 36 vii

viii

CONTENTS

Locations Other Characters Objects Previous Events Behavioural Components Perceived Behaviour Personality Traits Motivations Authorship Components Market Authors Textual Authors Summary References 3

The Corpus and Sample Choosing Doom Inclusion and Exclusion Criteria Inclusion Criteria Exclusion Criteria Identifying the Corpus Sources for Comics Texts Other Types of Text Selecting a Sample Sample Significance and Representativeness Sampling Methodology The Signifier Survey Survey Design Setting Up the Online Survey Finding an Audience Cleaning the Data Signifier Survey Analysis Your Experience of Doctor Doom About Doctor Doom Doctor Doom’s World Creators and Marketing Anything Else Signifier Survey Conclusion Effectiveness of the Survey Overall Results Using the Results to Generate a List of Signifiers Appearance Names and Titles Physical Actions

36 37 38 38 38 39 39 39 39 40 40 40 42 51 51 55 55 58 59 59 64 65 65 69 71 73 74 75 76 80 81 85 89 95 96 97 97 98 99 99 100 100

CONTENTS

4

ix

Behaviour Dialogue Other Characters Objects Locations Previous Events Textual Authors Market Authors References

100 100 101 101 101 101 101 101 102

Analysis Collecting the Data Database Design Data Entry Names and Titles Physical Actions Dialogue Locations Other Characters Objects Previous Events Perceived Behaviour Personality Traits Motivation Market Authors Textual Authors Data Entry Conclusion Analysis of Character-Specific Components Appearance Names and Titles Physical Actions Dialogue Summary of Character-Specific Components Analysis of Storyworld-Specific Components Other Characters Objects Settings Previous Events Summary of Storyworld-Specific Components Analysis of Behavioural Components Perceived Behaviour Behaviour—BFI Motivations

109 109 110 112 113 113 113 114 114 115 115 116 116 116 117 117 118 119 119 121 123 128 131 131 132 140 142 148 152 152 152 160 167

x

CONTENTS

Summary of Behavioural Components Analysis of Authorship Components Market Authors Textual Authors Summary of Authorship Components Conclusion References

170 172 172 175 177 177 179

5

A Tale Of Two Menaces The Sample Data Entry Character-Specific Components Appearance Names and Titles Physical Actions Dialogue Character-Specific Summary Storyworld-Specific Components Other Characters Objects Settings Previous Events Storyworld-Specific Summary Behavioural Components Perceived Behaviour BFI Motivation Behavioural Summary Authorial Components Market Authors Textual Authors Authorial Summary Overall Analysis References

183 184 186 187 187 188 189 191 191 192 192 194 195 196 196 197 197 197 199 199 200 200 200 201 201 202

6

Discussion The Corpus and Sample Data Design and Data Entry Data Analysis The Signifier Survey Comparison with Main Analysis Conclusion Does the Catalogue Work? Using Doctor Doom

205 207 210 213 214 215 221 221 223

CONTENTS

Donald Goes Dutch Other Possibilities References

xi

225 226 228

Appendix A: Using the Unified Model of Transmedia Character Coherence

231

Appendix B: Doctor Doom Corpus

235

Appendix C: Example of Signifier Survey

249

Bibliography

263

Index

293

List of Figures

Fig. 2.1 Fig. 2.2 Fig. 2.3 Fig. 2.4

Fig. 2.5 Fig. 3.1 Fig. Fig. Fig. Fig. Fig. Fig. Fig. Fig. Fig.

3.2 3.3 3.4 3.5 3.6 4.1 4.2 4.3 4.4

Fig. Fig. Fig. Fig. Fig. Fig. Fig. Fig.

4.6 4.5 4.7 4.8 4.9 4.10 4.11 4.12

Fig. 4.13 Fig. 4.14

A gleeful Doctor Doom in Spidey Super Stories #53 10 item short version of the BFI Doom speaks his motivation out loud in Fantastic Four #59 [139] Spider-Man’s motivation to help runs into J Jonah Jameson’s perception of him as a publicity-seeking phony in Amazing Spider-Man #1 [141] Relationship between dimensions and sources in unified catalogue of transmedia character components Doctor Doom as seen in The Official Handbook of the Marvel Universe #3 [10] Number of different series for leading super-villains, by year Sample page from the online survey Tweet asking for help with survey Experience of media types by users Familiarity by decade Jack Kirby and Joe Sinnott depict Doom in Fantastic Four #85 Credits box for Fantastic Four #237 [16] Doom’s gun comes and goes in Fantastic Four #10 Doom striking power poses in Astonishing Tales #1 [35] and Dazzler #3 [36] Dialogue groupings by period Frequency of recoded dialogue groupings Dialogue by media type Other characters per text by period Adjusted number of different characters featured per period Average number of characters per text by interaction and period Grouped number of characters per text by period Average number of characters per text by interaction and media type Grouped number of characters per text by media type Average number of previous events per text, by period

28 30 31

32 42 53 63 75 77 82 82 112 117 120 124 129 129 130 134 135 136 136 138 139 149 xiii

xiv Fig. Fig. Fig. Fig. Fig. Fig. Fig. Fig. Fig. Fig. Fig. Fig. Fig. Fig. Fig.

LIST OF FIGURES

4.15 4.16 4.17 4.18 4.19 4.20 4.21 4.22 4.23 4.24 4.25 4.26 4.27 4.28 4.29

Fig. 4.30 Fig. 5.1 Fig. 5.2 Fig. 5.3 Fig. 6.1 Fig. 6.2

Number of previous events per text by period Further recoded descriptions Recoded behaviours by period Period by recoded behaviours Recoded behaviours by media Openness to experience Conscientiousness Extraversion Agreeableness Neuroticism Neuroticism by media type Neuroticism for comics in each period Recoded motivations by period Recoded motivations by media type Doom joins forces with Henry Kissinger in Super-Villain Team-Up #7 [73] Logo for Curtis circulation (‘CC’) in top left corner of The Invincible Iron Man #74 [78] Dennis The Menace from the UK [3] (left) and USA [4] (right) British Dennis receives a beating from his father [14] American Dennis talks to Mr. Wilson as an equal [17] Theoretical underpinning of the unified catalogue of transmedia character components Doom (bottom right) as part of a Secret Wars recap in The Spectacular Spider-Man #111 [18]

150 155 157 157 159 162 162 163 164 164 165 166 168 169 171 173 184 190 192 206 218

List of Tables

Table Table Table Table Table Table Table Table Table Table Table Table Table Table Table Table Table Table Table Table Table Table Table Table Table Table Table Table Table Table Table Table

3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 3.9 3.10 3.11 3.12 3.13 3.14 3.15 3.16 3.17 3.18 3.19 3.20 3.21 4.1 4.2 4.3 4.4 4.5 4.6 4.7 4.8 4.9 4.10 4.11

Definition of ‘The Marvel Age’ and its sub-periods Recoded titles of texts in which Doctor Doom appeared Corpus texts by period Sample texts by period and media type First ten texts in corpus in new randomised order Survey response recoded Initial coding for ‘Names And Titles’ Awareness of comics series Awareness of animated TV shows featuring Doctor Doom Awareness of video games featuring Doctor Doom Aspects of Doctor Doom’s appearance Names and titles for Doctor Doom Physical actions Behaviours Things that Doctor Doom says Other characters Objects Locations Previous responses Creators Marketing Aspects of appearance Names and titles by number of texts Names and titles by period Names and titles by media type Physical actions Physical actions across time periods Physical action signifiers per text, by period Physical actions by media type Dialogue overall and by time period Other characters interacted with by number of texts Other characters by period

57 68 68 69 70 78 79 83 84 85 86 87 88 89 90 91 92 93 94 95 96 119 121 122 123 124 125 126 127 128 133 134 xv

xvi

LIST OF TABLES

Table Table Table Table Table Table Table Table Table Table Table Table Table Table Table Table Table Table Table Table Table Table Table Table Table Table Table Table Table

4.12 4.13 4.14 4.15 4.16 4.17 4.18 4.19 4.20 4.21 4.22 4.23 4.24 4.25 4.26 4.27 4.28 4.29 5.1 5.2 5.3 5.4 5.5 5.6 5.7 5.8 5.9 5.10 5.11

Other characters by media type Objects by number of texts Objects by media type Settings that appear in at least 10% of texts Settings by period Settings interacted with by period Ranked settings by media type Previous events by texts Previous events by media type Most popularly used descriptions of Doom Recoded descriptions of Doom Perceived behaviours by period Perceived behaviours by media Overall results for Doom’s big five personality traits Recoded motivations across all texts Recoded market authors Recoded market authors by media type Textual authors with 5 or more texts Dates for sample of UK and US Dennis The Menace strips Appearance Physical actions Dialogue recoded Other characters (US) Other characters (UK) Objects Settings Perceived behaviour for Dennis The Menace recoded BFI scores (with MAD) Menace motivations

137 140 142 143 144 145 146 148 151 153 154 156 158 160 168 172 174 175 186 188 189 191 193 193 194 195 197 198 199

CHAPTER 1

Introduction

This book sets out to define a straightforward methodology for empirically analysing transmedia characters as they move through time and across different media types. The methodology was created so that researchers could use it to create datasets and perform statistical analyses on transmedia characters in a way that could then be shared and compared with the results of other studies, in the hope that this will generate further research and insights into such characters. The Marvel Comics character Doctor Doom was chosen as the main case study for three reasons. Firstly, Doctor Doom is a wandering character who has moved between different series and media ever since he was first created. The fact that he has usually been portrayed as a ‘super-villain’ means that almost all of his appearances have been as a guest character, rarely maintaining a series of his own. This allowed him to develop independently of a specific creator or creative team in much the same way that modern transmedia characters are developed by large teams rather than a single ‘auteur’. This was true of several characters within the early years of the Marvel storyworld but, as Douglas Wolk has said, ‘Doom was absolutely the most interesting’ [1]. Secondly, Doctor Doom is not Batman. There is much to say about Batman’s long history of appearances in comics, films, television shows, radio and all other forms of popular media for the past eighty years, but a great deal of this has already been said in books such as The Many Lives of The Batman, Batman Unmasked: Analyzing a Cultural Icon, Many More Lives of The Batman and countless other chapters, journal articles and conference presentations [2–4]. By contrast there are very few outputs concerning Doctor Doom, and those that do exist concentrate on aspects of his fictional character—his disability, ethnicity or villainy—rather than his transmediality [5–7]. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 M. Hibbett, Data and Doctor Doom, Palgrave Studies in Comics and Graphic Novels, https://doi.org/10.1007/978-3-031-45173-7_1

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This, however, is likely to change if he is used in the long-awaited Marvel Studios Fantastic Four reboot [8, 9]. The final reason for choosing Doom was a purely personal one. As a lifelong comics fan, one of my favourite series is John Byrne’s lengthy run on Fantastic Four from 1981 to 1986, particularly those issues featuring Doctor Doom. This run of comics is one of the few aspects of my teenage comics collection that I have retained to the present day, and my initial plan was to use this project as an excuse to re-read and hopefully re-enjoy many of those texts. However, like so many of Doctor Doom’s own schemes, this did not come to pass as planned—the fact that I eventually identified a corpus of 266 texts containing Doctor Doom meant that it would be several years before I was able to read any of John Byrne’s run. This personal history with the character, and superhero comics overall, means that I fall under Matt Hill’s definition of an ‘Aca-Fan’—a scholar working within a field which they also happen to be a fan of [10]. An Aca-Fan can take an auto-ethnographic approach, using their own experiences as part of their methodology [11]. This was certainly the case with my process blog, Marvel Age Doom, which was used as way of note-taking, with most entries consisting of a review of a single text taken from the corpus [12]. It thus contained a mixture of academic analysis and my own personal opinions of the texts, especially for the later comics where I discussed my own memories of buying and reading them when they were first published. I have generally tried to keep these remembrances out of this text as a whole, but must admit that they do slip in occasionally, especially in the chapter ‘A Tale of Two Menaces’. Rather than examining one character across media, this chapter further tests the catalogue by using it to compare the British and American characters who share the name ‘Dennis The Menace’, looking for differences and similarities between them. With its focus on a character originated and primarily developed in comics, and a research corpus dominated by comics texts, this book therefore sits within the field of Comics Studies, and especially the sub-section related to superhero comics. Definitions of comics and Comics Studies are discussed in the ‘Overall Methodology’ chapter, focusing on the history of superhero comics as a genre, specifically the use and meaning of super-villains such as Doctor Doom. Comics Studies is an inherently interdisciplinary field, existing partly within the digital humanities ‘at the intersection of digital technologies and humanities’ and this interdisciplinary use of different methodologies is also discussed in this chapter [13]. Special attention is given to the use of empirical methodologies, which is a growing area of interest in Comics Studies, and has been employed to quantitatively analyse diverse topics such as the structure of comics, audience reception, corporate identities, and corpus construction among many others [14–20]. One of the key methodologies used in this book is Transmedia Theory. Henry Jenkins’ original definition of transmedia storytelling stated that it involved a storyworld that operates ‘across multiple media platforms, with

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3

each new text making a distinctive and valuable contribution to the whole’ [21]. The Marvel storyworld of this period was not transmedial under this definition. Stories did branch out across many different texts but they always operated transtextually, staying within the same media type. For example, the storyline featuring Doctor Doom that began in Daredevil #36 [22] continued in that series up until Daredevil #38 [23] before concluding in Fantastic Four #73 [24], which contained links to Thor #150 [25] and an appearance by Spider-Man. However, none of these texts shared any story with the contemporaneous Fantastic Four [26] or Spider-Man [27] cartoon series, nor did these latter pair have any links to each other, as they existed in entirely separate storyworlds. Even today, although the Marvel Cinematic Universe of the twenty-first century links together movies, television series, video games and, occasionally, one-off comics set within that storyworld, it never interacts with the still-ongoing storyworld of the main comics line. However, theories of ’Transmedia Archaeology’ argue that earlier forms of transmedia storytelling did not rely on shared storyworlds operating across different media, but were instead focused on individual characters who retained their core aspects across media even as the worlds around them changed [28]. Examples of this included silent movie characters like Charlie Chaplin’s ‘little tramp’ or earlier comics characters such as The Yellow Kid and Ally Sloper, all of whom carried their core identity with them in a way that was variable but recognisable, whatever storyworld they were placed in [29–33]. The characters themselves were transmedial, even when their storyworlds were not. There are many fascinating, thought-provoking research outputs which describe ways in which questions about such transmediality could be approached, but they are often frustratingly abstract. My own professional background is in data-led research, largely medical statistics and epidemiology, where clear methodologies exist to get clear answers to research questions. Even where researchers cannot be entirely sure that the answers arrived at are unarguably correct (as is often the case in statistics) there are ways to work out exactly how confident one can be about them [34]. There are even practices such as Bayesian Statistics which not only allow for but actually include personal bias as a quantifiable part of the research process [35]. By contrast many transmedia theories appear to be generated either as philosophical discussion points or personal attempts to quantify thoughts about specific characters, rather than practical tools which other researchers might be able to usefully employ. Thus this chapter concludes with an attempt to find a solution to this issue by bringing key theories together into a unified catalogue defined in such a way that it can be used as a practical tool to empirically measure transmedia character coherence. With the unified catalogue of transmedia character components defined, the next chapter attempts to set out a way to test it and then use it in practice. The first part, ‘Choosing Doom’, explains the rationale for using this character as a case study. Here the inclusion and exclusion criteria for the generation of

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a study corpus are explained, including the proviso that the corpus be taken from a specific period in time, rather than being left open-ended or relying on some other criteria, and the process by which the time period of ‘The Marvel Age’ was chosen is detailed here. This is followed by a description of how the corpus was actually identified, using a mixture of online databases, internet searches and a close reading of texts. The eventual corpus contained 266 texts, mostly comprised of comics but also including cartoons, games, radio plays and other media. This was too large a dataset to analyse in full using the unified catalogue of transmedia character components, and so the next section, ‘Selecting a sample’ explains how sampling was undertaken in order to reduce the corpus down to a representative sample of 69 texts. At this point the initial analysis of the texts began, but I very quickly found that I was relying on my own knowledge of Doctor Doom to decide what should be recorded, running the risk of simply confirming my own pre-existing, non-empirical, beliefs. For example, I knew that Doctor Doom regularly called people ‘dolt’ and wore a green cloak, so would always be on the lookout for these signifiers. However, if he had other signifiers that I was not already aware of—for instance, his use of viewing screens or regular meetings with Namor The Sub-Mariner—I would not necessarily realise that these were worth recording. This problem was tackled by conducting a survey which asked a range of other people familiar with Doctor Doom to give their impressions of him. The intention here was that this would expose me to a much wider range of characteristics which could then be used as a starting point for the main analysis. With all of this completed the task of analysing the corpus could finally begin. To facilitate this an online database system was built into which data about the text themselves could be entered, based on the structure of the unified catalogue of transmedia character components. The process of developing the database and entering data is described at the beginning of the ‘Analysis’ chapter, including an explanation of difficulties encountered for each of the thirteen character dimensions that formed the catalogue. The rest of this chapter details the analyses of each of the four subsets of components, i.e. those relating to the character, their storyworld, their behaviour within that storyworld and finally the real-world authors of the texts. For each grouping the components are analysed by media type and time period. With the analysis of Doctor Doom complete, a new question arose—can this tool be used for other characters or has it been designed in such a way that it only works for its original purpose? To answer this the chapter ‘A Tale of Two Menaces’ moves from the Master of Menace to the American and British characters who share the name Dennis The Menace. This chapter looks at a much smaller corpus of texts and thus less data is analysed, as it is designed as a test of the tool rather than an in-depth analysis of the two characters, yet it is still able to offer some interesting results.

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After this the ‘Discussion’ examines the overall processes used and asks whether the catalogue itself was an effective tool. Here the findings from the Doctor Doom analysis are compared to those from the signifier survey, examining the differences between the two and discussing the benefits of using an empirical tool such as this as opposed to relying on the qualitative findings of one or more individuals. The chapter, and the book as a whole, concludes with suggestions of ways in which the catalogue could be employed. My great hope is that other researchers will be able to use this catalogue to analyse other characters and then, by making the resulting datasets available, compare these character analyses alongside each other, creating an ever-growing dataset which can be used to examine the question of character coherence in new and more informative ways. In order to encourage others to do so I have included a summary of the tool, including instructions on how to use it, in Appendix A. Similarly, all of the data generated in this book, including the survey responses, main Doctor Doom dataset and smaller Dennis The Menace dataset, has been made available Open Access at https://doi.org/10.25441/arts.c.6140805.

References 1. Wolk, Douglas. 2021. The Master Plan of Doctor Doom! 22 April. Accessed April 23, 2021. https://voiceoflatveria.com/2021/04/20/9-the-master-plan-ofdoctor-doom-with-patrick-a-reed/. 2. Pearson, Roberta, and William Uricchio. 1991. The Many Lives of the Batman: Critical Approaches to a Superhero and His Media. London: Routledge. 3. Brooker, Will. 2001. Batman Unmasked: Analyzing a Cultural Icon. London: Bloomsbury. 4. Pearson, Roberta, William Uricchio, and Will Brooker. 2015. Many More Lives of the Batman. London: Palgrave. 5. Alaniz, José. 2015. Death, Disability, and the Superhero: The Silver Age and Beyond. Mississippi: University Press of Mississippi. 6. Rueber, Micah. 2014. “The Man in the Gray Metal Suit. Dr. Doom, the Fantastic Four, and the Costs of Conformity.” In Comics as History, Comics as Literature. Roles of the Comic Book in Scholarship, Society, and Entertainment, edited by Annessa Ann Babic, 157–170. Madison: Fairleigh Dickinson University Press. 7. Terjesen, Andrew. 2009. “Why Doctor Doom Is Better Than the Authority.” In Supervillains and Philosophy, edited by Ben Dyer, 81–90. Chicago: Open Court. 8. Wolk, Douglas. 2021. Voice of Latveria. Accessed June 2, 2021. https://voiceo flatveria.com/. 9. Keane, Sean. 2021. Fantastic Four: Everything We Know About the Team’s Marvel Cinematic Universe Debut. Accessed July 27, 2021. https://www.cnet. com/how-to/fantastic-four-everything-we-know-about-teams-marvel-cinematicuniverse-debut/. 10. Hills, Matt. 2002. Fan Cultures. London: Taylor and Francis. 11. Lamerichs, Nicolle. 2018. Productive Fandom: Intermediality and Affective Reception in Fan Cultures. Amsterdam: Amsterdam University Press.

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12. Hibbett, Mark. 2017–2021. Marvel Age Doom (Process Blog). Accessed December 22, 2020. http://mjhibbett.co.uk/doom. 13. Terras, Melissa. 2011. Quantifying Digital Humanities. London: UCL Centre for Digital Humanities. https://www.ucl.ac.uk/infostudies/melissa-terras/Digita lHumanitiesInfographic.pdf. 14. Pérez, Jon. 2009. “The Composition and Structure of the Comic.” Journal of Quantitative Linguistics 16 (4): 327–353. 15. Bateman, John A., Francisco O. D. Veloso, Janina Wildfeuer, Felix HiuLaam Cheung, and Nancy Songdan Guo. 2017. “An Open Multilevel Classification Scheme for the Visual Layout of Comics and Graphic Novels. Motivation and Design.” Digital Scholarship in the Humanities 32 (3): 476–510. 16. York, Chris. 2018. “The Form of Nostalgia: John Byrne’s Fantastic Four and Frank Miller’s Daredevil.” Bournemouth: Ninth International Graphic Novel and Comics Conference. 17. Meesters, Gert. 2017. “Comics in Newspapers in Belgium.” Dundee: Eighth International Graphic Novel, Comics Conference and Bandes Dessinées Society Conference. 18. van den Anker, Marinus J. J., and Piet Verhoeven. 2014. “Corporate Communication: Analysing Marvel and DC.” Studies in Comics 5 (1): 117–129. 19. Dunst, Alexander, Jochen Laubrock, and Janina Wildfeuer. 2018. Empirical Comics Research. 1st ed. Oxon: Routledge. 20. Dallavalle, Sara. 2021. Comics Magazines: Not Only Comics but Also Criticism. London: Transitions 9. 21. Jenkins, Henry. 2008. Convergence Culture: Where Old and New Media Collide, 95–96. New York: New York University Press. 22. Lee, Stan, Gene Colan, and Paul Reinman. 1968. “The Name of the Game Is... Mayhem!” Daredevil (Marvel Comics) 1 (36). 23. Lee, Stan, Gene Colan, and Frank Giacoia. 1968. “The Living Prison!” Daredevil (Marvel Comics) 1 (38). 24. Lee, Stan, Jack Kirby, and Joe Sinnott. 1968. “The Flames of Battle --.” Fantastic Four (Marvel Comics) 1 (73). 25. Lee, Stan, Jack Kirby, and Vince Colletta. 1968. “Even in Death….” Thor (Marvel Comics) 1 (150). 26. 1967. Fantastic Four. Directed by William Hanna and Joseph Barbera. Produced by Hanna-Barbera Productions. 27. 1967–1970. Spider-Man. Directed by Grantray-Lawrence. Produced by GrantrayLawrence. 28. Scolari, Carlos, Paolo Bertetti, and Matthew Freeman. 2014. Transmedia Archaeology: Storytelling in the Borderlines of Science Fiction, Comics and Pulp Magazines. Basingstoke: Palgrave Macmillan UK. 29. Sabin, Roger. 2003. “Ally Sloper: The First Comics Superstar.” Image & Narrative, Online Magazine of the Visual Narrative. 30. Fingeroth, Danny. 2004. Superman on the Counch. New York: Continuum. 31. Scott, Jason. 2009. “The Character-Oriented Franchise: Promotion and Exploitation of Pre-Sold Characters in American Film, 1913–1950.” In Cultural Borrowings: Appropriation, Reworking, Transformation, edited by Ian Robert Smith, 34–55. Nottingham: Scope.

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32. Bertetti, Paolo. 2018. “Buck Rogers in the 25th Century: Transmedia Extensions of a Pulp Hero.” In De/Recontextualizing Characters: Media Convergence and Pre-/Meta-Narrative Character Circulation. Tuebingen, Germany: De/Recontextualizing Characters: Media Convergence and Pre-/Meta-Narrative Character Circulation. 33. Meyer, Christina. 2019. Producing Mass Entertainment: The Serial Life of the Yellow Kid. Columbus: Ohio State University Press. 34. Walker, Ian. 2010. Research Methods and Statistics. Basingstoke: Palgrave Macmillan. 35. Field, Andy. 2016. An Adventure in Statistics. London: Sage.

CHAPTER 2

Methodology

This chapter will look at the key methodologies and theories that underpin the research in this book. Although Doctor Doom is presented here as a transmedia character the vast majority of the texts in which he appeared were comics, and so it makes sense to begin with a brief examination of the field of Comics Studies.

Comics Studies As the name suggests, Comics Studies is the study of comics, although the definition of ‘comics’ has been argued over for decades [1]. The different interpretations have largely been attempts to cope with what Aaron Meskin calls ‘hard cases’, i.e. ‘cases where there is legitimate controversy about whether the item in question is actually a comic or not’ [2]. However, as the main examples used here will be comics texts which were published by Marvel Comics and produced by professional comics creators working within the comics industry, they can safely be considered to be ‘easy cases’ of comics. The specific type of comic found in these examples is superhero comics, a genre which has its roots in the ‘American Monomyth’ whereby a lone figure emerges to save a community from the forces of invading evil before disappearing again without requiring thanks [3]. This is neatly encapsulated in the theme tune to the Spider-Man cartoon series [4], which states that ‘At the scene of the crime/ Like a streak of light / He arrives just in time’ and that ‘Wealth and fame he’s ignored / Action is his reward’ [5]. The appreciation and study of comics has a long history, but it is only in the twenty-first century that Comics Studies has begun to establish itself as

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 M. Hibbett, Data and Doctor Doom, Palgrave Studies in Comics and Graphic Novels, https://doi.org/10.1007/978-3-031-45173-7_2

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an academic field, with dedicated courses, conferences and journals, largely as a response to the perceived respectability that graphic novels began to enjoy in the 1980s [6, 7]. Comics scholars tend to come to the topic via other academic departments, such as English, Film and Media Studies, Sociology, Art and Design [8]. This means that it has had little time to develop its own unique tools, but ‘continues to rely on terminologies and theories handed down from other disciplines’ [9]. Comics Studies can thus be described as ‘interdisciplinary’.

Interdisciplinary Research ‘Interdisciplinary Research’ is an approach which is ‘a synthesis of disciplinary knowledge and methods’, an ‘analytically reflective study of the methodological, theoretical, and institutional implications of implementing interdisciplinary approaches to teaching and research’ [10, 11]. As comics themselves are a combination of text and illustration, the field of Comics Studies has been described as ‘inherently interdisciplinary’ [12]. This term ‘interdisciplinary’ is currently much in favour in many different forms of research, and some argue that it has become ‘a buzzword of faculty and administrators which has been appropriated to describe so many academic pursuits that it is virtually meaningless’ [13, 14]. In the field of Comics Studies, ‘interdisciplinarity’ tends to be deployed ‘as a descriptive label, serving merely to acknowledge the variety of disciplines from which the field’s contributing scholars hail’ [15]. It has been described as a ‘magpie theory of interdisciplinarity’ which ‘cobbles methods, subjects, and texts from various disciplines’ and ‘marks off the arrangements as a new unified mode of inquiry’ [16]. For better or for worse, this is the approach that will be taken in this book, as existing tools from disciplines such as psychology, computing and statistics are brought together in order to create new tools. Transmedia methodology will be discussed in the next chapter, and most other methods will be described as they occur, but some methodologies that were central to the project need to be explained before going any further, beginning with empirical research.

Empirical Research The term ‘empirical research’ refers to any kind of research which is drawn from empirical evidence, i.e. ‘based on direct experience or observation of the world… rather than, for example, by theorizing, or by reasoning, or by arguing from first principle’ [17]. Within Comics Studies this means using ‘a set of methods capable of supporting or falsifying its hypotheses about the medium of comics, often constructed by theoretically and methodologically fine-grained analyses, with the help of empirical testing and quantitative corpus studies’ [18]. For our purposes here it will primarily be used as a combination of ‘applied computer

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science with humanistic scholarship’, using computing techniques to generate and analyse a corpus for the close reading of individual texts [18]. The combination of close reading with database-led methods of corpus analysis is a comparatively recent innovation in Comics Studies, and may even be an example of the field developing a unique tool of its own, with several studies now using such methods to generate and analyse large datasets [19]. The most well-known example of this approach is probably the What Were Comics ? project which seeks to develop ‘a data-driven history of the American comic book’ by moving away from abstract humanistic questions such as ‘what are comics?’ towards the empirically grounded ‘what were comics?’ [20]. What Were Comics ? takes a random sample of 2% of all comics published in the US between 1934 and 2019, and then uses close reading to record formal and material elements from both text and paratext that will enable analysis of the corpus as a whole, thus answering the eponymous question ‘what were comics?’. The Visual Language Research Corpus is another data-driven corpus analysis which contains coded information from 36,000 panels in 290 comics texts, entered by a group of nine independent coders [21]. The data collected includes ‘coding of panel framing, semantic relations between panels, external compositional structure (page layout), multimodality, and a variety of other structures of visual languages’ [22]. Other examples of studies using databases to analyse formal aspects of comics texts include the MediaDB project, an attempt to ‘provide researchers, students and fans with a comprehensive searchable library of all the French-language magazines of comic art news and criticism published since the 1960s’, and The Graphic Narrative Corpus , a dataset of around 250 graphic novels, memoirs, and non-fiction written in English [23, 24]. These projects concentrate on formal elements of the texts, whereas Julia Round’s Misty Database combines this approach with an examination of narrative content, with data about the publication history of individual stories and their creators sitting alongside a system of coding for story types, using bespoke categorisations such as ‘reader has been misled through medium’, ‘moral/uplifting’ and ‘shock twist/inversion to story’ [25]. The Claremont Run takes a similar approach to Chris Claremont’s tenure as writer of The Uncanny X-Men, providing a dataset for the quantitative analysis of ‘structure, characterization and representation’ in ‘a comics story that was 16 years in the making’ [26]. As with the Misty Database, its coding is closely tied to its specific content, such as instances of hugging, expressing reluctance to fight, or being declared dead. Like many of these projects, the analysis in this book is mostly arranged around a specific theme—in this case the presence of Doctor Doom during a specific period of time. However it differs from other projects in two important ways. Firstly, the coding and classification systems developed here are intended for use with all characters and storyworlds, not just the specific characters and storyworlds of the project within which it originates. For example,

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data collected by The Claremont Run records whether individual characters are ‘subject to torture’ or are seen ‘flying with another character’ and other categories which recur in that corpus, but are unlikely to be useful for other researchers examining other characters. Secondly, the system developed will not be restricted to specific types of text. This book will use the unified catalogue of transmedia character components to examine television programmes, radio shows and cartoons, but it can be used for any type of storytelling. Some components will be used more often than others in certain media types, but this would be true whatever texts are examined. For example, it is likely that a radio show would not contain as much information about a character’s appearance as a graphic narrative such as a cartoon or comic, but if an aspect of a character’s appearance was important one might expect this to be conveyed somehow, such as through a description by another character. Similarly, a very brief appearance by a character would not contain as much information overall as a lengthier one, but some information about them would still be conveyed, and the catalogue could be used to examine which particular components were deemed important enough to still be featured. It might be that future advances in technology enable other components to arise which are not yet included in the unified catalogue of transmedia character components. The way a character smells is not explicitly dealt with, for example, because it is very rarely mentioned as a character component, but if new forms of storytelling emerged which allowed for smells to be reproduced this might change.

Grounded Theory Choosing which components to record was initially extremely complicated, with several attempts to do so quickly confounded by contact with the actual data. It soon became clear that this was an iterative process requiring constant re-evaluation, and thus grounded theory became one of the primary research methods for the entire project, forming the framework within which everything else would fit. Grounded theory is a research method designed to assist the development of new theories [27]. It was originally designed by Barney Glaser and Anselm Strauss for use in qualitative research and described in their book Discovery of Grounded Theory as ‘the discovery of theory from data’ [28]. Since then, however, it has become a widely used research method across a wide range of disciplines and subject areas, both qualitative and quantitative [29]. The core concept of grounded theory is that of a continuing process of categorisation, with the developing theory based ‘not in an intangible, abstract form, but rooted, or grounded, in systematic observation’ [30]. The new theory thus emerges iteratively from the information gathered rather than relying on pre-assumed analytical/theoretical constructs, gradually becoming finessed as new information is received [27].

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Under grounded theory researchers begin with the data, identifying and coding categories of information as they work through it. Broad ‘low-level’ categories are identified first but, as the researcher learns more about the data, more specific ‘high-level’ category labels are devised. For instance, a reading of character types in superhero literature might begin with simple types such as ‘superhero’ or ‘super-villain’ and then develop into more nuanced categories such as ‘mutant’, ‘sidekick’, ‘dark avenger’ or ‘anti-hero’. This was especially relevant for the development of the thirteen-dimensional model of character coherence. An initial version of this model contained only eight dimensions, and significant data entry was undertaken based on the assumption that this was sufficient. However, so many problems arose as a result of this that both the model and the data entry system had to be completely re-developed. This was grounded theory in action—new information arose from interaction with the actual data, forcing iterative revisions to the theoretical concepts before returning to the data. However, although it was vital to the development of ideas and methods, it made initial attempts to describe this process extremely difficult. Research in Arts and Design is generally considered to be complex, iterative and ‘messy’, but the constant movement back and forward between theory and practice, especially the continuing changes to the model, made describing the process as it happened more complex, iterative and messy than any reader could be expected to cope with [31]. With that in mind the description of methods has been streamlined so that, although the construction of the transmedia model and the data entry systems occurred in tandem, each informing the other, their final shapes are described separately, with transmedia following here while the data systems are discussed in the next chapter.

Existing Models of Transmedia Characters There are many different models for defining a transmedia character, but each one misses out key aspects identified by others. However, by bringing them together into a single unified catalogue of transmedia character components a tool can be developed which can then be used to map a character’s coherence as they move across time and media. This tool can then be applied in various ways, both qualitative and quantitative. Later chapters will discuss how it was used in this project to define categorisations for the gathering of quantitative data for statistical analysis, but before that the unified catalogue of transmedia character components will be defined through an examination of existing models. Transmedia Storytelling Henry Jenkins coined the term ‘transmedia storytelling’ in 2003 to describe a way to develop content that ‘would play well across media’ [32, 33]. He went

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on to further define transmedia storytelling in his book Convergence Culture as ‘the art of world making’, whereby storyworlds are created which can contain many narratives, unfolding ‘across multiple media platforms, with each new text making a distinctive and valuable contribution to the whole’ [34]. Jenkins’ definition has become a cornerstone of the field of transmedia studies, with his work cited widely—Convergence Culture, for example, has been cited over 18,000 times [35]. While Jenkins is regarded as ‘perhaps the most pivotal figure in developing transmedia studies in the humanities to date’ the word ‘transmedia’ had been used by earlier scholars [36]. For example, in Playing with Power in Movies, Television and Video Games Martha Kinder talked of children experiencing ‘an ever-expanding supersystem of entertainment, one marked by transmedia intertextuality’ [37]. Here she discussed the way that the content of different texts interact with each other, not just in similar texts—such as the revival of Looney Tunes cartoons referring to aspects of the original series fifty years earlier—but across media, such as advertisements for Teenage Mutant Ninja action figures appearing next to the television series. These texts refer to each other, but do not necessarily exist within the same narrative storyworld, or in the case of toys contain a narrative at all. The ‘world making’ referred to by Jenkins requires some form of seriality, building worlds and characters over time in what Matthew Freeman calls ‘expansive intertextuality’ where stories can be added to by offering new information, new events or new viewpoints [33]. The shared ‘universe’ of Marvel comics was an early example of this, creating a storyworld containing many narratives that interacted with each other, albeit a storyworld that initially existed in a single medium [38, 39]. The ‘Marvel Universe’ subsequently grew into a massive fictional storyworld containing many stories, all of which interact with each other in what Matt Hills has called a ‘hyperdiegesis’: a ‘vast and detailed narrative space, only a fraction of which is ever directly seen or encountered within the text, but which nonetheless appears to operate according to principles of internal logic and extension’ [40]. This kind of storyworld can also be described as ‘transtextual’, in that it operates across different texts which relate and refer to each other, as described by Gérard Genette in The Architext: An Introduction [41]. Genette splits transtextuality into five separate categories, as follows: Intertextuality—one work referencing another. Paratextuality—text that surrounds the text, such as titles, advertisements and footnotes. Archetextuality—the conventions which place the text within its genre. Metatextuality—a critical commentary by the text upon itself. Hypertextuality—what links the text to other texts [42].

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The Marvel Universe clearly operates fully transtextually, with its references to previous stories (intertextual), use of editorial footnotes (paratextual), use of genre tropes (archetextual), constant use of in-jokes and messages from the editorial team (metatextual) and links from one story to the next (hypertextual). It also operates transfictionally, in that it features ‘the migration of fictional entities across different texts that do not necessarily share the same author’ [43]. Examples of storyworlds that comply with Jenkins’ definition of ‘transmedia’ include some of the most commercially successful entertainment franchises of the last forty years, such as Star Wars , Planet of the Apes (in both its original and rebooted series), The Lord of the Rings , Warcraft , Harry Potter and the ‘Marvel Cinematic Universe’ of the twenty-first century. Narratives in these storyworlds not only move between different publications, but different media formats too, such as video games, books, toys, live events and comics, with fans focusing on the storyworld ‘through’ whatever media format it is presented in, without being concerned about the transfer from one to another [44]. As Martin Barker has stated, it is the storyworld, not the format that matters—‘the medium really doesn’t have to be the message’ [45]. Under Jenkins’ definition of transmedia, these storyworld franchises work as a unified system for delivering stories which are able to maintain ontological security—‘a sense of continuity and order in events’—throughout [46]. If, for example, a character dies in a Harry Potter book, then fans would expect that this would be respected in a subsequent book, game or stage play. However, while maintaining a stable continuity may be fairly straightforward in a series based on books written by a single author, it becomes much more complicated in a shared universe produced by hundreds of creators in multiple monthly instalments over more than half a century [47]. Storyworlds such as these are not generated by the ‘planned, strategic aspects of creation’ which are the focus of studies of modern, digital-based forms of transmedia, but instead emerge over time and by many hands [48]. Thus, while Jenkins’ definition of transmedia storytelling may be considered the cornerstone for the field, it is clear that there are many aspects of transmedia which it does not adequately cover. For instance, many other scholars have taken different approaches, examining areas such as branding and franchising, fan consumption, pre-digital transmedia ventures, and the nature of characters within such storyworlds [49–51]. All of these fields of enquiry provide valuable insight, but as this research is to do with transmedia characters, and particularly ‘early’ pre-digital characters such as Doctor Doom and, later, the two versions of Dennis The Menace, the particular approach it will use is that of transmedia archaeology. Transmedia Archaeology and Early Transmedia Characters In order for a storyworld to be considered as transmedia under Jenkins’ definition it is required to exist in different media but the same storyworld, with

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a single hyperdiegesis operating across different types of text. This definition focuses on digital storyworlds of the twenty-first century, largely ignoring the pre-digital history of transmedia development. As Colin Harvey suggests, ‘transmedia storytelling is more fruitfully understood as a broad category to describe instances of convergent storytelling, but also varieties of pre-digital, licensed tie-in production that anticipate convergence’ [52]. The theory of ‘Transmedia Archaeology’ argues that the history of transmedia storytelling stretches back much further than the digital franchises which Jenkins initially described and can be seen in earlier forms of storytelling which focused on individual characters operating across different media, rather than storyworlds [51]. These early transmedia characters appear in many different media in unrelated stories where specific parts of their background or personality might differ, but their core aspects remain true throughout. For instance, Tarzan is recognisably Tarzan throughout multiple iterations in books, serials, films and comics [33]. Certain aspects may change—the age at which he was left in the jungle, his relative ability to speak English, his supporting cast—but his essential ‘Tarzan-ness’ remains [53]. Jason Scott has similarly suggested that the ‘character-oriented franchises’ of the silent movie era are examples of early transmedia, with the same characters appearing again and again in different environments, retaining the essence of their characters despite the changes in the world around them [54]. Many of the early stars of movies also appeared in new adventures in comic strips, notably Charlie Chaplin who appeared in several strips around the world, some appearing many decades after his death [55]. Other examples of early transmedia include the Japanese ‘Media Mix’ strategy of storytelling across different media, often said to originate in 1963 when the Astro Boy manga was adapted into a live action TV series and, subsequently, cartoons, films, video games and merchandising [56]. Long before this, the promotional items and live theatre shows developed around The Yellow Kid and Ally Sloper at the end of the nineteenth century also fit into this idea of a transmedia character, while there is a long history of characters from novels, such as Mary Shelley’s creature from Frankenstein, being placed on stage, with or without the permission of the author [57–59]. In all of these cases the transmedia franchise was built around the character rather than the world that they inhabited, and so the characters themselves carried the burden of ontological security, by retaining a core identity that was variable but recognisable [60, 61]. The term ‘early transmedia character’ can therefore be understood as referring to a character who carries their storyworld with them and, as Jenkins himself has recognised, predates the digital storyworlds of the late twentieth and twenty-first centuries [60, 62].

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Characters Carrying the Storyworld A notable example of this appears in the Superman/Spider-Man team-up Marvel Treasury Edition #28 [63]. Here both lead characters bring multiple aspects of their storyworlds with them, including specific locations, previous events, other characters and ways of behaving. Doctor Doom is also involved in this story and brings aspects of his own storyworld into the story, notably the Latverian Embassy. These ‘crossovers’, where characters who do not usually interact are placed together, have been ‘central to the economic model of comic book publishers since at least the mid-1980s’ and in recent years have become a selling point across other superhero media as well [64]. The ‘Crisis On Infinite Earths’ crossover which took place across the CW’s ‘Arrowverse’ group of TV shows in 2019 and 2020 made a virtue of this, not just featuring cameos from previous TV versions of DC characters, but including additional aspects of their original storyworlds too. This included different versions of the character The Flash from the TV series The Flash [65], Justice League movie [66] and the then-current The Flash [67] all appearing in their original costumes, and having supporting characters such as Lois Lane appearing alongside the Clark Kent from Smallville [68]. This event was so well received that similar crossovers have been repeated several times since. Going even further, the Marvel Comics Spider-Verse crossover purported to feature all known versions of Spider-Man meeting each other, often using different artistic styles to denote different storyworlds, notably in Spider-Verse Team-Up #2 [69] where the comics version of Miles Morales, first seen in Ultimate Fallout #4 [70], and Peter Parker from the Ultimate Spider-Man cartoon[71] visit the world of the original Spider-Man cartoon series [4], with all being drawn in differing art styles to match their original storyworlds. This stylistic choice was also used in the Spider-Man: Into the SpiderVerse movie [72], loosely based on the comics series, with characters such as Spider-Man Noir appearing in black and white and Spider-Ham resembling a Looney Tunes cartoon. However, for the most part these were characters who had been invented as additional, previously unseen, alternate versions for the original comics series, rather than from actual pre-existing transmedia worlds. This practice of illustrating new characters in styles which pastiche older comics also has precedents in superhero storytelling, notably in Alan Moorescripted series such as 1963, Promethea and especially Supreme. In Supreme Moore attempted to create a new character with an invented publishing history similar to that of Superman, with multiple ‘prior’ versions of the character each denoted by an artistic style similar to the period that was being referenced, all existing in a post-reboot afterlife called ‘The Supremacy’ [73].

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Global Transmedia Character Networks Jan-Noël Thon has described this use of different versions of the same character as a ‘Global Transmedia Character Network’—an assemblage of character versions from single or ‘local’ works and ‘global transmedia’ or serial works, across different media and media types [74, 75]. To take Doctor Doom as an example, his Global Transmedia Character Network includes the various cartoon, movie, game, book, newspaper and other versions of the character which exist alongside, and share characteristics with, the comics version, but are not generally understood to be the same character with necessarily the same backstory or character traits [76]. Thon describes how different versions of a character may come together in the opinion of the ‘recipients’ (i.e. readers, viewers, consumers, etc.), and that these recipients apply ‘charity’ to the different versions to ‘forgive’ paradoxes in the network. Asking why Doom’s tunic, for instance, is a different colour in a cartoon and a comic would be asking one of Kendall Walton’s ‘silly questions’, which are ‘irrelevant to the appreciation’ of the text in question—in other words, readers suspend their disbelief [77]. For example, when the Tomb Raider [78] franchise was rebooted with a new game fans of the franchise ‘forgave’ differences between the backstory of the original and new versions of Lara Croft, considering both to be part of the same Global Transmedia ‘Lara Croft’ Network. They were able to do this because the core ‘Lara Croft-ness’ of the character remained intact [75]. Such fans are happy to hold all of these ‘various, often mutually incompatible interpretations’ of their favourite characters in their heads without confusion, so long as enough of the core signifiers remain [79]. Fans are thus able to construct their own favoured version of the character which may not directly correspond to any single ‘official’ iteration, but will still be able to sit alongside them in the network. Unlike Mark Wolf’s concept of ‘canonicity’, whereby a person in authority (such as the intellectual property owner or a noted author) declares what ‘counts’ for the character, a Global Transmedia Network is a bottom-up, rather than top-down, approach whereby the recipients decide what belongs within the network [75, 80]. It is instead similar to Tony Bennett and Janet Woollacott’s idea of ‘texts of Bond’, where the prevalent current version of a character (James Bond in their example) alters over time as older versions interact with newer, transmedia additions to the network [81]. One criticism of this idea is that some fans will never apply charity, and will always ask ‘silly questions’. A notorious example of this in Doctor Who fandom was the issue of the ‘UNIT dating controversy’ [82]. Briefly, stories concerning the Third Doctor and UNIT were broadcast in the 1970s but set in the 1980s. However, a later story broadcast in the actual 1980s confused this by being set in the then-present day but showing UNIT’s leader The Brigadier to have retired some years earlier, contradicting the original

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continuity. Rather than charitably ignoring these problems, some fans-turnedwriters of the Doctor Who New Adventures series of novels later in that decade used their stories as an arena to put forward their own, often contradictory, views on when these stories should be set [83]. Rather than seeking to solve the problem once and for all, the television series made a joke of it, with The Sontaran Stratagem episode having The Doctor recall working for UNIT ‘Back in the ’70s. Or was it the ’80s?’ [84]. Despite this, I believe the general point is true, that as long as the core aspects of the character remain, most audience members will decide to apply charity. Paolo Bertetti refers to this decision-making on the part of the audience as ‘a tension between coherence and incoherence, or between continuity and discontinuity, depending on whether the features identifying the characters at the various levels remain unchanged or vary from one text to another’ [85]. Fans use loaded terms like ‘canonicity’ and ‘continuity’ to denote which stories ‘count’ and which do not, applying value to the former over the latter, but the idea of characters within a Global Transmedia Character Network having varying degrees of ‘coherence’ relative to the rest of the network is free of such connotations, and so is the term that will be used here [86]. All of this discussion raises the question of what it is about a fictional character that makes them recognisable as a specific entity, whether in Jenkins’ idea of a coherent storyworld acting across different media or Thon’s network of related but individual entities. One way to describe this is through the concept of ‘character signifiers’. Character Signifiers The idea that a character could have particular ‘signifiers’ that identify them across different media predates Henry Jenkins’ theories of transmedia. In their book The Many Lives of Batman [87] Roberta Pearson and William Uricchio examine the signifiers of Batman which allow him to retain his ‘Batmanness’ in whatever media he appears in, codifying these into five components: attributes, events, recurrent characters, setting and iconography. Examples of signifiers for Batman within these components could be as follows: Attributes—wealth, physical prowess, deductive skills, obsession with crime. Events—the murder of his parents, ongoing quest to fight crime. Recurrent Characters—Robin, Alfred, the Joker. Setting—Gotham city. Iconography—mask with pointed ears, cape, bat insignia on chest. Will Brooker identifies a similar list of signifiers as ‘a raft to cling to’ when examining Batman as a character who ‘undergoes so many transformations,

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and is subject to so many competing, often contradictory interpretations, that any defining essence sometimes seems eroded’ [88]. As with Pearson and Uricchio, he argues that all of these characteristics are required for Batman truly to be Batman, but recognises that each can be stretched to some degree. For instance, Batman’s costume has changed in many ways over the years, but he almost always retains at least the core ingredients such as a mask or cape, and while supporting characters come and go, and even die, key ones such as Robin, Alfred and Joker will always return. Alan Moore identifies similar aspects as part of ‘the character’s mythology’ in his introduction to Frank Miller’s The Dark Knight Returns [89], listing Alfred, Robin and the Joker as recurring characters, Gotham as the location, and Batman’s use of gadgets in his fight against crime [90]. Umberto Eco described this kind of fictional existence as an ‘oneiric climate’, whereby certain aspects of a character, such as their name, appearance and supporting cast, are always the same at the start and end of the story, but could vary wildly in between, with little or no relationship to past or future events [91]. He used the Superman comics of the 1960s as examples, but this could also apply to many other transmedia characters, with Sherlock Holmes, for example, carrying similar expectations for readers. However, there are differences between such literary characters and superhero characters like Superman, Spider-Man or indeed Doctor Doom. Firstly, comics characters have a pictorial basis often missing from literary characters which make them much easier to recognise, and also to copyright and trademark [92, 93]. Indeed, American case law has established that ‘characters who were flatter, consistent, memorable, and easily removed from the story or context in which they were created ultimately received more [copyright] protection than more complex and fully human characters, and this makes such characters more attractive for transmedial adaptations’ [94]. Secondly, modern superhero characters tend to operate in extended ‘universes’—transtextual and sometimes transmedial storyworlds containing many narratives that interact with each other over time [38, 39]. Characters existing in such storyworlds require clearly defined ‘signifiers’ so that they can easily be borrowed by different creators. This is especially true of super-villains such as Doctor Doom, who act as story generators within the superhero genre, able to instigate action and plot wherever they are placed [95, 96]. A rough idea of Doctor Doom’s signifiers expressed in terms of Pearson and Uricchio’s classification of components might be: Attributes—mastery of magic and science, arrogance, intelligence. Events—the deaths of his parents, the accident which scarred his face. Recurrent Characters—The Fantastic Four, Boris, Namor. Setting—Latveria, New York. Iconography—steel mask, green hood, cloak and tunic.

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This is an approximation based only on my own opinions and reading history. As will be shown later, other readers have very different ideas of what defines Doom, and both these viewpoints differ again from the eventual results gained from the unified catalogue of transmedia character components. Filling in examples for these five types of component is fairly straightforward as an exercise in its own right, but issues immediately arise when trying to apply this idea to actual texts. It is easy enough to name events or settings, but less so when trying to fit all aspects of Doctor Doom’s character into the schema. For example, where does Doom’s Time Machine fit in? Is it an attribute, or a setting, or something else which is not included here? Is the fact that he speaks in the third person an attribute, or, in a text where speech appears as printed word, is it iconography? To be fair to Pearson and Uricchio’s codification, it was developed for the purposes of talking specifically about Batman in a book about Batman, and not for use as a practical tool for a wider examination of other characters or their storyworlds, and so it should not be a surprise that it does not function as such. However, as will be seen, similar problems occur with other schema which set out to be used more generally. Storyworld Components Where Pearson and Uricchio suggest a codification of the components of a character, Klastrup and Tosca propose something similar for storyworlds, which they define as ‘an abstract content system from which a repertoire of fictional stories can be actualised or derived’ [97]. They state that a storyworld has the following three key features: Topos—the setting of the world, in terms of time period and geography. Mythos—the backstory of the world, which provides the knowledge needed to interpret events within it. Ethos—the code of ethics, which provides characters with the knowledge required to know how to behave in the storyworld. The Marvel Universe can be defined as a storyworld fairly easily using this scheme: Topos—‘the world outside your window’ [98]. Although the Marvel Universe stretches from the beginnings of time to the far future, and expands across this and other dimensions, the core aspects of the storyworld are set in a version of the modern world, particularly modern Manhattan. Mythos—the key difference between this world and ours is that its physics allows superpowers to exist. Magic and science work side by side, and extra-terrestrial invasions are considered a part of everyday life.

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Ethos—those who work, for the most part, for the maintenance of the status quo are considered superheroes, those who work against it supervillains. As before, this definition is based on a personal and partial recollection of the Marvel Universe over my own lifetime. It has no empirical evidence to back it up, and other researchers might well provide entirely different definitions based on their own personal experience. If we accept the idea that transmedia characters, and particularly ‘early transmedia characters’, carry their own storyworld with them then Klastrup and Tosca’s definition of the storyworld must therefore also form a part of the definition of transmedia characters [60]. For example, the character-specific signifiers of the Disney character Goofy may vary in many ways across time and media, but there are certain aspects of his world which always remain the same, such as the fact that it has a ‘mythos’ in which a non-human character can act in a human-like way [99]. Including such storyworld components as part of a transmedia character’s overall components thus seems logical, although as with Pearson and Uricchio’s definition there is much here that is missing due to the original intent of the classification i.e. Klastrup and Tosca’s definition contains little relating to characters themselves purely because the classification is designed for describing entire storyworlds. A more serious criticism of their model relates to the language used. Giving components unique names which can then be re-used as terminology is sensible, but for someone who is not a classically trained scholar words such as ‘Topos’, ‘Mythos’ and ‘Ethos’ are largely meaningless, and arguably exclusionary. From my own experience, attempting to use such non-intuitive terminology in active research proved extremely difficult. Even after several years using these terms I was still often forced to check which was which, and trying to explain them to other scholars would often leave everybody confused. As will be shown, these experiences informed the decision to use clearer labelling for components in the final unified catalogue of transmedia character components. Story/Worlds/Media Another useful definition of storyworld components comes from Marie-Laurie Ryan’s Story/Worlds/Media: Tuning the Instruments of a Media-Conscious Narratology [100]. Here Ryan brings together many aspects seen in both Pearson and Uricchio’s and Klastrup and Tosca’s codifications, as follows: Existents—the characters and significant objects that exist within the plot. Setting—the space within which the existents are located.

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Physical laws—the ‘laws of nature’ which determine the kind of events which are practically possible within the story. Social rules and values—the ‘laws of man’ which frame the characters’ behaviours. Events—the physical events which occur in the time span framed by the narrative. Mental events—the motivations which cause events to happen and the emotional reactions to them. Ryan’s ‘setting’ matches with Pearson and Uricchio’s ‘setting’, as well as to the time period and places contained within Klastrup and Tosca’s ‘topos’. Her ‘Physical laws’ aligns with their ‘mythos’, while ‘social Rules and values’ matches ‘ethos’ and ‘events’ matches Pearson and Uricchio’s component of the same name. The fact that Ryan’s definition fits with a combination of the first two definitions discussed suggests that this is a reliable way to define storyworlds. However, there are two components within Ryan’s definition which do not match up with the others so neatly. The ‘characters’ aspect of her component ‘existents’ is captured within ‘recurrent characters’, but what she calls ‘significant objects’ are not. These are part of what Geraint D’Arcy has referred to as the ‘Mise en scéne’ of comics—the way that the arrangement of characters and objects within a location or setting contributes to create meaning [101]. It could be argued that these objects might fit therefore within ‘setting’, but Ryan explicitly separates these out in her own definition, and my own experience during data entry confirms that this is appropriate. There are definitely specific objects which are part of Doctor Doom’s set of signifiers—‘viewing screens’, for example, appear regularly in his stories and are an important signifier of his character, but are not settings, nor are they other characters. Thus they need to be included in any overarching set of signifiers, and require their own classification of ‘objects’ as a component. Similarly, while Ryan’s ‘mental events’ could perhaps be placed within Pearson and Uricchio’s ‘attributes’, engaging with data entry showed that ideas of ‘motivation’ and ‘emotional reactions’ were too complicated to be contained in this way. For example, it is generally straightforward to use this system to codify ‘Existents’, as other characters and objects can be identified in the texts as they go along, but ‘mental events’ are much more difficult to code for fictional characters who do not actually have a mentality for events to happen in. As will be discussed shortly, these required more thought and a different approach to categorisation. In common with other classifications, actually trying to use these codifications on a sample of texts showed that they were most likely designed primarily as discussion points, rather than as practical tools. This is entirely understandable in an academic paper but is not hugely helpful for data entry,

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hence the need for further investigation into how ideas like ‘mental events’ can be assessed empirically. Existential and Fictional Identity Paolo Bertetti proposes a definition of a transmedia character that is divided into two different main types of identity: existential and fictional [99]. These can be roughly defined as who a character is and what they do, respectively, sub-divided as follows: Existential identity: This refers to the aspects of the character that exist apart from a narrative—who they are, rather than what they do. Bertetti divides this into Proper and Relational identities. Proper—aspects of the character themselves, such as their appearance, personal qualities, names, visual appearance and societal roles. Bertetti further divides this identity into ‘figurative’ and ‘thematic’ identities. Figurative—items which apply to the character themselves, such as their appearance and name. Thematic—the roles that the character plays within the text, such as their job. Relational—relationships to other characters and to the world around them in space and time, sub-divided again into: Actorial—relationships with other characters, such as parent, enemy, or mentor. Spatial/temporal—their place in space or time. Fictional identity: This covers aspects of the character that occur when they are placed within a narrative and start to ‘do’ things and is again sub-divided, as follows: Actantial—the actions that the character undertakes within the fiction. Modal—the motivations behind these actions. Axiological—the deep values that lead to these motivations and actions. The earlier criticism of Klastrup and Tosca for using non-intuitive language applies even more so here. Over the course of my own research I eventually found Bertetti’s system of definition to be one of the most comprehensive, but it took a long time to realise this because of the difficulty in getting to grips with the language he uses. For example, in ‘Fictional identity’ the terms ‘Actantial’, ‘Modal’ and ‘Axiological’ would be much easier to understand if they were renamed ‘Actions’, ‘Motivations’ and ‘Values’. As with Klastrup and Tosca’s terminology, the need to provide unique terminology is understandable, and it is clear that scholars who have knowledge of the disciplines from

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which these terms originate would have no such difficulties, but to be truly useful a tool should not use terminology that requires constant and repeated reference to a glossary. As with Ryan’s definition, many aspects of Bertetti’s Proper and Relational identities can be mapped onto other versions of transmedia components. For existential identity, proper identity is mostly contained within Pearson and Uricchio’s attributes and iconography, while much of relational identity falls within their recurrent characters and setting as well as Klastrup and Tosca’s topos. In both cases, however, Bertetti identifies components that require further investigation. For example, although much of his fictional identity is contained within Klastrup and Tosca’s ethos and mythos, it also suggests a need to consider the character’s motivations for their actions, in line with Ryan’s mental events. Bertetti’s model includes other new items which need to be taken into account, notably the character’s name. Doctor Doom is not just referred to as ‘Doom’ or ‘Doctor Doom’, but goes by several other names and titles which are repeated across his texts and are part of the acceptance that he is indeed Doctor Doom. Sometimes he is never referred to as ‘Doctor Doom’ at all— the mysteriously bandaged figure in Invaders #32 [102] for example shows none of the usual signifiers of Doom’s appearance and is only ever called ‘Victor’, but is clearly meant to be Doctor Doom. Similarly, when he appears in Not Brand Echh he is almost always referred to as ‘Doctor Bloom’. Another aspect of the figurative identity which has not been covered by any of the models discussed so far is the way that a character speaks. An important signifier of Doctor Doom is that he generally speaks in the third person and insults other characters, and this can even be a plot point when he is in disguise, or somebody else is pretending to be him, pointing the reader towards a hidden identity. One way to record this would be to simply type in all of a character’s dialogue and then analyse it later. This would have some similarities to corpus linguistics analysis (CLA), a methodology which uses computer systems to analyse large volumes of textual data by, for example, counting how many times specific words appear within a text, or how often certain groups of words are found in proximity to each other [103]. However, CLA treats the words within a text purely as data to which a quantitative statistical analysis can be applied, operated across the entire text or corpus as a whole [104]. In this project the purpose is to examine words as narrative content, with meanings and relationships to other aspects, and to identify them over time and across media, rather than as a whole. This is not to say that the statistical analyses similar to CLA would not be possible within this approach—they would, if one was prepared to type in every single word in every single text—but that is not the focus here. Instead, it seems sensible to add both ‘objects’ and ‘speech’ to the growing list of character signifiers. Issues of codification are more complicated when it comes to Bertetti’s fictional identity, which is related to Ryan’s mental events via their discussion

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of values and motivations. Again, engaging in data entry demonstrated that these factors could not be coded in the same way as others, but in trying to find an answer to this problem a much wider question was uncovered around ways to quantify the behaviour of a fictional character. Behaviour The way in which a character behaves is part of all character-based transmedia definitions, and yet finding a way to codify this is extremely difficult. As this sub-section will show, the eventual answer to this problem is not to treat it as a single component at all, but rather a separate category of components covering different aspects of overall behaviour. Firstly, it is important to note that the methodologies discussed will be used to analyse fictional characters as fictional characters, in that they serve to provide a categorisation of the storytelling aspects which identify the different versions of the character rather than as a psychological evaluation of a ‘real’ person. Psychological analyses of superheroes are very popular, from mass market text books like Superheroes: An Unauthorized Exploration [105], Superman on the Couch [61] and Batman and Psychology: A Dark and Stormy Knight [106] to comic series such as Watchmen [107] or Powers [108], which selfconsciously set out to discover what make superheroes ‘tick’. However, none of these characters actually have a personality to analyse—they are all ‘an imagined person with an imagined life’ [109]. They appear to have a personality due to the work of a succession of creative workers interpreted by readers, viewers and/or listeners over many years [110]. In the words of Uri Margolin, each one is a ‘non-actual individual… freely devised or constructed by an actual human mind’ or, in this case, minds [111]. The practical problems with codifying a fictional character’s personality became clear during the earliest attempts to enter data about Doom’s behaviour. The first iteration of data entry involved using open-ended text fields to enter a description of the character’s general behaviour, which was entirely unsatisfactory as it was impossible to generate empirical, reproducible data. For example, in Fantastic Four #198 [112] I could have described Doom’s behaviour as any of ‘arrogant’, ‘rude’, ‘megalomaniac’, ‘cares for his son’, ‘hateful’, ‘bitter’, ‘vain’, ‘grudgeful’, ‘intelligent’ or ‘unforgiving’. However, this would be based entirely on my own view of his behaviour, and anyone else might have a very different opinion and use entirely different words to describe them. Also, either of us might code them differently again if the task was undertaken at a different time in a different emotional state. This difficulty was demonstrated when I conducted a survey in which I asked superhero fans to describe Doctor Doom. The category ‘behaviour’ drew far more unique responses than any other aspect of the character, with 233 distinct descriptions, 150 of which were suggested by only one respondent each [113].

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Happily for me, this problem of describing personalities has long been an issue in the field of psychology, going back to at least 1936 when Allport & Odbert identified 18,000 words to describe personalities, and it has continued up to the present day as researchers attempt to formulate their own dictionaries of terms [114–116]. So, in order to address this I chose to use three different approaches to describing personalities, based on the psycholexical approach, psychological character traits, and the idea of ‘motivation’ taken from creative writing studies. The Psycholexical Approach The psycholexical approach is a methodology based on analysing descriptive terms used to describe someone in a text, on the assumption that this can ‘provide sufficient information for individuals to describe themselves and others at a relatively granular level within a social context’ [117]. In other words, it uses the way that characters, real or fictional, are described by themselves or others as a basis for personality analysis. This is generally done using text-mining, ‘the process of extracting valuable information and knowledge from unstructured text’ [118]. Here textual data is extracted from texts such as speeches and novels and analysed in order to codify the personalities of the people described within [117, 119]. When this methodology has been used to analyse fictional characters before it has been argued that the resulting analyses are more likely to indicate the personality of the creator, rather than the character [120]. While this might be the case when applied to characters who only appear within the works of a single creator, a transmedia character who exists across different texts and media, with different creative teams over time, could theoretically have a personality which is independent of individual creators, and thus discernible from the way they are described in these texts. For example, stories featuring characters such as Sherlock Holmes, Batman, Lara Croft, Conan, James Bond or Doctor Who have all been developed by multiple creators over many decades, but audiences are still able to recognise the character so long as their behaviour does not stray too far from what has been established previously [121]. Using a psycholexical approach to collect together the words used to describe Doom’s behaviour in a sample should therefore generate an unbiased, replicable dataset. For instance, in Fantastic Four #198 [112] the following words and phrases are used in the text to describe Doctor Doom: ‘proud’, ‘powerful’, ‘despot’, ‘ingenious’, ‘ruler’, ‘good’, ‘carefree’, ‘haughty’, ‘high and mighty’, ‘hateful’, ‘stoppable’. If a psycholexical methodology was followed, these same terms would be collected by anybody analysing this text. However, this methodology does miss important information. In the story above, Doom clearly behaves throughout like an arrogant megalomaniac who despises his subjects, but this is not captured via the psycholexical approach, where he is never described using these words or any like them. This is

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Fig. 2.1 A gleeful Doctor Doom in Spidey Super Stories #53

even more noticeable in other texts that use far fewer descriptive words. For example, in Spidey Super Stories #53 [122] there are no words used to describe Doom’s behaviour at all, so no analysis can be carried out despite the fact that he demonstrates character traits such as arrogance and megalomania, alongside less often seen ones such as delight and glee (Fig. 2.1). This is not a problem unique to this category of signifier, as other categories of data may be missing for all types of signifier across the corpus, but here it seems that there is information being put across about behaviour which cannot be harvested using this methodology. To put this another way, if we do not see Doctor Doom’s gauntlets in a specific text then they are simply not being used to signify his character. However, if Doom behaves in a specific way but it is not described by himself, the narrator or other characters, then that important data is being missed. Thus, other approaches are needed to collect this information. Personality Traits and Characters One possible answer to this problem would be to assess Doom’s behaviour in terms of his personality. In the field of psychology personalities are described using traits—‘relatively enduring characteristics that influence our behaviour across many situations’ [116]. Traits are the defining features of who we are (our personality) which determine what we do (our behaviour)— a differentiation that is reflected in Bertetti’s existential and fictional identities [85]. The most widely used, and widely agreed upon, current method for categorising personality traits is the Big Five Inventory [123–125]. This methodology groups personality traits into factors of openness to experience,

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conscientiousness, extraversion, agreeableness and neuroticism—often referred to with the acronym OCEAN [126–129]. Each factor can include sub-factors, with words arranged on a scale so that, for example under ‘Extraversion’ someone could be described on a scale from ‘talkative’ to ‘silent’, ‘frank’ to ‘secretive’, ‘adventurous’ to ‘cautious’ and ‘sociable’ to ‘reclusive’ [130]. Difficulties have been noted with the use of the Big Five, especially that it is language-specific, and thus requires different scales for different languages, or even additional traits, but there is a general consensus that it is a useful tool within the English language context [115, 131]. It has also been shown to be an effective method of assessing the personalities of fictional characters [132]. Many surveys have been developed based on the Big Five, which allow users to assess their own or other people’s personality traits through a series of questions about their behaviour [133]. These surveys vary in size from ten questions up to 300 [134, 135]. Questions are generally posed by asking respondents to describe themselves or others in relation to how accurate certain statements are about the person in question. Thus there is a choice to make about the size of survey to use to capture this information. Attempting to complete a fifty-question survey about a character for every text would be time consuming and difficult, partly due to the number of questions, but also the nature of the questions—for example, question 23 asks for a rating of the statement ‘Get chores done right away’, which is unlikely to be accurately answerable for most superhero texts. Shorter surveys are available which use broader questions and are quicker to undertake, and these have been shown to give results comparable with longer versions, especially when used for text rather than in-person or telephone interviews [136]. The disadvantage of these shorter scales is that they do not allow for subfactors, instead giving simple numeric data for each of the five main factors, but in this case such a simplified system can actually be beneficial, allowing for straightforward analysis of large datasets. For these reasons I chose to use the 10 Item Short Version of the BFI for this research [134]. As the name suggests, this contains just ten items, as shown in Fig. 2.2. The version shown above is for self-assessment. When it is being used about someone else, or a fictional character, the opening question is rephrased as ‘I see [name] as someone who…’. Once the questions are answered the results are combined into the Big Five Personality traits by adding the results of the questions together in pairs as follows: Extraversion: Answer to question 1 (reversed) plus answer to question 6. Agreeableness: 2 + 7 (reversed). Conscientiousness: 3 (reversed) + 8. Neuroticism: 4 (reversed) + 9. Openness to Experience: 5 (reversed) + 10.

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Fig. 2.2 10 item short version of the BFI

For example, if in a certain text I disagreed strongly that the character ‘is reserved’ and disagreed a little that they had ‘few artistic interests’ it would be coded as a score of 7 for ‘Extraversion’, made by adding together the reversed score for question 1 (5) and the score for question 5 (2). Thus, by completing this survey a five-part behavioural score can be generated for each text, which can then be used to analyse how much the character’s behaviour changes or remains consistent across time and media. There are, however, two problems with this methodology. First of all, it relies upon there being sufficient data expressed in a text. When a character appears very briefly in a text without speaking or doing anything it would be impossible to answer the above questions based purely on events in the text itself. Secondly, by its nature the BFI still does not cover important specifics of certain characters. Doctor Doom may score highly on ‘Conscientiousness’, for example, but this does not indicate that what he is most conscientious about is the development of schemes to conquer the world. Character traits do not, therefore, supply a single answer to the problem of recording behaviour, and even combining it with the psycholexical approach would leave gaps. A further approach is therefore necessary. Motivation As this is an attempt to codify the behaviour of fictional characters it seemed sensible to investigate theories derived from creative writing theory to see if they could be of use in describing their behaviour. F Scott Fitzgerald’s famous statement ‘Action is character’ is often referred to in this field, meaning that what matters in a fictional narrative is what the character actually does [137]. However, for a character to be believable these actions must have some sort of motivation—a reason for doing what they do

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[138]. If, in real people, personality can be described as ‘characteristics that influence our behaviour’ then by this theory the personality of fictional characters could be described prosaically as the ascribed motivations that produce actions [116]. Attempts to mine data about motivations from sample texts showed that this was a much more straightforward process than attempting to categorise nebulous ‘personalities’, especially in comics texts where motivations are often expressed in thought bubbles or, indeed, are spoken out loud. Different audiences might have different ideas about why Doctor Doom may wish to conquer the world, but in most cases it is clear that this is his motivation (Fig. 2.3). As with other versions of behaviour, motivations cannot be discerned in every text. In many texts, for example, Doom is shown as part of a montage of characters, used to represent all super-villains rather than as an active agent of the plot, but in these cases it does not matter. The fact that no motivations are ‘shown’ is a valid piece of data to record—we are not missing any data on motivation, because there is no motivation to record. However, other aspects of Doom might well be included. We may not have access to Doom’s inner motivations, but we might have a description of his character as a ‘tyrant’. This demonstrates that these two ways of describing character behaviour are in fact complementary—the way that the character is described, whether by other characters, the narrator or indeed themselves, is different information to whatever their inner motivation is. Laurie Hutzler describes these two different aspects as ‘The Mask’ and ‘The True Self’ [140]. The mask is the way that the character is presented to the outside world— the equivalent of their described behaviours—while the true self is what really drives them—their motivations. These interact with each other, along with other story components including other characters and the world itself, to drive stories forward.

Fig. 2.3 Doom speaks his motivation out loud in Fantastic Four #59 [139]

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Behaviour Summary Put together this demonstrates that ‘behaviour’ is a far more complicated item to codify than what a character looks like or which other characters they interact with. It also shows the strength of using grounded theory to develop these ideas, as most of the complications were identified through data entry, rather than just through consideration of theory. The three different approaches detailed here each have strengths and weaknesses, but each captures a slightly different aspect of the character. The psycholexical approach tells us how the character is viewed by other characters within the storyworld, and sometimes the creators of the work too, which may be very different from the actual personality traits which that character exhibits. A good example of this would be the early adventures of Spider-Man, where Peter Parker is shown to be a shy, neurotic teenager riddled with guilt whose motivation is to atone for the death of his uncle, but who is regularly described by other characters as a glory-hunter and menace to society. The opposition of these views is a vital part of Spider-Man’s character (Fig. 2.4). These three different ways of looking at behaviour are reflected in aspects of Paolo Bertetti’s character components. The way that the character is perceived, expressed psycholexically within the text, is a way of empirically

Fig. 2.4 Spider-Man’s motivation to help runs into J Jonah Jameson’s perception of him as a publicity-seeking phony in Amazing Spider-Man #1 [141]

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describing Bertetti’s actorial identity—their relationship with other characters. Their motivations are similarly contained with Bertetti’s modal identity, while their analysable character traits are an empirical expression of the ‘deep values’ contained within axiological identity. Thus it makes sense to use all three of these character components, and to group them together as a separate category to describe the interaction of the character with other aspects of the storyworld which they inhabit. Character-Building, World-Building and Authorship Matthew Freeman, in his book Historicising Transmedia Storytelling, divides the components of a transmedia character into three separate categories [33]. The first two, ‘character-building’ and ‘world-building’ encompass much of what has already been discussed, with aspects which are specific to the character falling under ‘character-building’, while those which apply more generally to the storyworld belonging to ‘world-building’. However, Freeman’s third category, ‘authorship’, has not been covered by any of the previous descriptions of transmedia characters. He states that historical transmedia activities can only properly be understood by also examining the authors involved, because ‘in telling different parts of the same larger story, it is authors1 who of course dictate characters and entire fictional story worlds, building both of these aspects across multiple media’ [33]. Not only are these authors responsible for developing characters and storyworlds, but they also have the ability to apply ‘canonicity’ to what they have created—‘that is, its acceptance in the minds of certain audiences as a legitimate extension of a storyworld as opposed to an unauthorised or illegitimate addition, such as fan-fiction’ [33]. This involves Foucault’s ideas around an author’s name having ‘indicative functions’, so that for instance the name ‘George Lucas’ attached to a Star Wars text does not simply say that George Lucas was involved in it, but also implies an authoritative canonicity that, for example, a novel by another author would not [142]. A criticism of this view of authorship is that it privileges ideas of ‘malecentred, individual authorship that are historically descended from theorizations of the auteur’ [36]. By concentrating on a single guiding ‘genius’, such as a showrunner like Joss Whedon or franchise overseer like Marvel’s Kevin Feige, the many other contributors to a transmedia story are ignored. One way to deal with this issue is to split authorship into two ‘authorfunctions’: that of a market author-function and a textual author-function [33]. The market author-function relates to Foucault’s indicative function, focusing on the way that the presence of an author’s name guides readers to what is inside and to what other texts exist—in the world of comics this might also include having the company name on the cover, to alert readers to the 1 Here the word ‘author’ applies to all creators of the text, rather than simply the ‘writer’ or ‘scripter’.

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storyworld within. The textual author-function relates to the power individual authors have over the storyworld they are in the act of creating. For a character like Spider-Man the market authors would include Marvel comics, whose corporate ownership is proclaimed on the cover, but also Stan Lee, who ‘presents’ many of his texts, both in comics and other media appearances. This, along with the words ‘Marvel Comics’ on the cover, offers reassurance to a potential purchaser that what they will find inside is a genuine, canonical, Marvel comic. It is similar to the use of Edgar Rice Burroughs as part of the branding of Tarzan, or Ian Fleming in James Bond movies, even when the actual stories told are nothing to do with the original authors [143]. The textual authors however include not just the traditional writers and artists who fans might recognise as ‘creators’, such as Stan Lee, Steve Ditko, John Romita Sr., or Todd McFarlane, but also all of the letterers, colourists and editorial staff who also appear in the credits, like Sol Brodsky, Glynis Wein, Carol Potts, Louise Jones and Jim Shooter. As Freeman says, ‘if both character-building and world-building… are important to transmedia storytelling, then authorship is crucial for achieving both character-building and world-building’ [33]. In other words, for any kind of transmedia activity to occur, somebody—or something, such as a corporate body—needs to make it happen. The final section of this chapter will discuss how these two forms of authorship can be combined with the other transmedia character components to form a single model.

A Unified Catalogue of Transmedia Character Components This chapter has demonstrated that transmedia characters can be described using ‘character components’, and that there are many different ways to categorise them. It has also showed that there is significant overlap between the different definitions of components, and that these components can be grouped together depending on whether they refer to the character, their storyworld, their behaviour, or their authors. These different definitions will now be brought together into a single thirteen-dimensional model of transmedia characters using a version of Matthew Freeman’s three categories to group them into character, storyworld, and authorial components, along with a new fourth category called ‘behavioural’, which can be taken to describe the way that the character interacts with other aspects of their storyworld. To avoid the issues of confusing or exclusionary language, many of the components have been given new or simplified names with the hope that this will convey their meanings more clearly. In the explanations that follow examples are given of signifiers for wellknown characters. These are based purely on my own knowledge of the

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characters concerned rather than any attempt to define them using the methodologies described in later chapters. Character-Specific Components These components are concerned specifically with the character themselves. Appearance This corresponds directly with Pearson and Uricchio’s ‘iconography’ component and aspects of Bertetti’s ‘figurative identity’, concerning the visual appearance of the character. The word ‘appearance’ was chosen instead of ‘iconography’ because the latter is a loaded term with complex meanings within other fields, notably Art History, whereas ‘appearance’ can be simply taken to mean ‘what they look like’. It is placed first in this list of signifiers for comic characters because it is the most easily defined aspect of a twodimensional character and, as discussed earlier, it is this pictorial basis which makes them easier to copyright than other types of character [92, 93]. Examples Wonder Woman’s appearance includes signifiers such as a tiara, wrist amulets, red bustier, or black hair.

Names and Titles This records the names and titles used to refer to the character within the text and comes largely from Bertetti’s thematic identity. Examples Names and titles for The Doctor from Doctor Who would include ‘Doc’, ‘Professor’, ‘John Smith’, ‘The Oncoming Storm’ and ‘The Doctor’.

Physical Actions These are the physical actions particular to the character which occur within the text itself. This comes from Ryan’s physical laws and Bertetti’s actantial identity, as well as part of Pearson and Uricchio’s attributes. Examples Spider-Man’s physical actions include climbing walls, telling jokes, shooting webs and swinging through Manhattan.

Dialogue This final character-specific component contains the frequently used phrases, or catchphrases, which are associated with the character and appear regularly in their own speech, and is taken from Bertetti’s figurative identity.

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Examples The Thing uses phrases such as ‘it’s clobberin’ time’, ‘what a revoltin’ development’ and speaks in a stylised New York accent.

Storyworld-Specific Components These components are the aspects of the overall storyworld that the character interacts with. In a large ongoing storyworld such as the Marvel Universe the signifiers within these components can exist independently of the character in question. Spider-Man, for example, is a frequent signifier for Doctor Doom within ‘Other Characters’, but he also interacts with many other storyworld components in his own storylines. This component, then, does not cover the storyworld as a whole, but only the version of the storyworld that the character carries with them. Several additional issues arose during data entry for these components, often around the problem of whether all storyworld signifiers within a text should be recorded or only those that the character under investigation interacted with. Examples of such issues are given where applicable, along with the solutions used. Locations This component contains all of the settings which appear in the text, including both time and space. It is based on Pearson and Uricchio’s ‘settings’, Klastrup and Tosca’s ‘topos’, Ryan’s ‘settings’, and Bertetti’s ‘spatial/temporal identity’. Working with the corpus of texts featuring Doctor Doom highlighted a common issue with this category, especially for a supporting character like Doom who often appears only briefly within a story, in that many texts contain settings which he has no interaction with at all. This was especially common within comics from the 1970s, where creators like Gerry Conway, Roy Thomas and especially Chris Claremont would weave together several sub-plots in a single issue which would not necessarily join with the main plot for months, and sometimes never. For example, in Uncanny X-Men #145 [144] Storm visits the ballet. This occurs as part of the story which involves Doom, but Doom has nothing whatsoever to do with this location, so it could be argued that it does not signify him as a character. On the other hand, one might argue that all locations which occur in the same story as the character are relevant because they identify the wider storyworld which they exist in and therefore distinguish them from other characters from different storyworlds. For instance, the X-Men’s mansion also appears in the story alongside Doom—he never visits it, but it is part of the wider narrative and shows that both character and location exist within the wider Marvel Universe. This distinguishes him from fictional characters of other

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storyworlds—Paddington Bear, say, would not be expected to appear in a story that also features the X-Men’s mansion. Paolo Bertetti refers to this as ‘the timeline and the world depicted on the ‘mother ship’, the primary work which anchors the franchise’ [145]. Transmedia characters who exist within a storyworld carry all of the information about that storyworld with them as part of their own character. The solution to this was to record all locations within the specific story that Doom appears in, tagging each one to say whether or not he interacted with them, so that both versions could be analysed. Examples Doctor Doom interacts with locations such as Latveria, Manhattan and the United Nations.

Other Characters This component contains Pearson and Uricchio’s ‘recurrent characters’, the parts of Ryan’s ‘existents’ that refer to characters, and Bertetti’s ‘actorial identity’. As with all storyworld-specific components, this refers only to other characters who appear in the same story as the main character, not those in other parts of the text (such as other, unrelated, stories) or paratexts (such as letters pages or advertisements). For example, in Fantastic Four Annual #15 [146] only those characters who appeared on the cover alongside Doom, or within the back-up story ‘The Power of the People!’ were recorded, whereas those who only appeared in the lead, Doom-free, story ‘Time for the Prime Ten’ were not. This approach is taken due to the character-focused nature of this research, based on the idea that early transmedia characters carry their storyworld with them [51, 54]. The worlds of these characters may intersect with others, but the focus is on the aspects of that world which the character carries with them, in this case the signifiers which occur in the same stories as they do. This is especially important in a storyworld like the Marvel Universe where, in theory at least, all stories told within it are Doctor Doom stories, as well as SpiderMan stories, or Man-Thing stories and so on, as each of them is part of that world. As with locations, during data entry it was noted whether each character ‘interacted’ with Doom. For example, in Avengers #155 [147] there is a subplot involving the Beast, Whizzer and Wonder Man fighting Attuma and Namor, which does not intersect in any way with the main plot featuring Dr. Doom, even though both appear within the same story. Thus all five characters are logged as appearing in the story, though not interacting with Doom. Examples Harry Potter interacts with characters such as Hermione Granger, Ron Weasley, Dumbledore, Snape and Voldemort.

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Objects This component comes primarily from Ryan’s ‘existents’ as it applies to objects rather than characters, and is used to record all of the physical items which appear in the text that are part of the plot, which are not otherwise categorised as part of settings. For example, if the Hulk strode through Manhattan there might be automobiles in the background, but these would be part of the city location, rather than objects that take part in the story. However, this would change if Hulk picked one up and threw it at somebody. As with objects and locations, the character’s interaction with each object was also noted. Examples James Bond might interact with objects such as a gun, a sports car, a modified watch or a glass of Martini.

Previous Events This component contains Klastrup and Tosca’s ‘mythos’, Ryan’s ‘events’, and aspects of Pearson and Uricchio’s ‘events’, to mean all previous events which are directly mentioned within the text. Usually these would be events previously seen in earlier texts, sometimes referenced by footnotes, but in some cases this might apply to events which have not actually been seen before, such as a new aspect to an origin story. These events are part of the character’s own storyworld which they carry with them. The entire history of the overarching storyworld is not included in this component, unless they retrospectively become part of the character’s own storyworld. For instance, the adventures of Andrew Garfield’s version of Spider-Man were not part of the Tom Holland version’s storyworld until they were introduced in Spider-Man: No Way Home [76, 148]. Examples Previous events that are signifiers for Batman include the murder of his parents, having his back broken by Bane, seeing a bat fly through his window, or the death of Jason Todd.

Behavioural Components These components describe the way that the character interacts with the other aspects of the storyworld. Most previous definitions include this category as a single component within the character components category but, as discussed, grounded theory demonstrated that behaviour was too complex to be contained in this way. As well as adding clarity to what we mean by ‘behaviour’ this also has the advantage for a character-based model of transmedia of creating a way of examining how a specific character interacts with other parts of the storyworld, whether that be other characters, locations, objects or the world’s history. The three components within this category are as follows.

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Perceived Behaviour This component contains the way that the character is perceived, gathered empirically by recording all of the descriptive words applied to the character within the text. As well as descriptions given by other characters this also includes the perceptions of the character themselves as well as those given by editorial and narratorial voices within the story. Its application is taken from the psycholexical approach, and is an expression of Bertetti’s ‘actorial identity’, describing the character’s relationship with other characters. Examples Perceived behaviours for Doctor Doom include ‘mad man’, ‘genius and ‘cruel’.

Personality Traits This describes the way that the character’s personality is perceived by the consumer of the text, using the Big Five Inventory. It relates to Klastrup and Tosca’s ‘ethos’ and Ryan’s ‘social rules and values’, in that the character’s personality is an expression of the way that they interpret and navigate the storyworld’s code of ethics. It also aligns with Bertetti’s ‘axiological identity’ in that their personality is a result of their ‘deep values’. Examples Doctor Doom might score highly for extraversion, conscientiousness, neuroticism and openness to experience (in that he would loudly proclaim his meticulous plan to overcome the injustices placed against him using a new device he has invented) but low for agreeableness (in that he is, after all, Doctor Doom).

Motivations This component is what drives the character’s overall behaviour. It includes Ryan’s ‘mental events’, Bertetti’s ‘modal identity’ and aspects of Pearson and Uricchio’s ‘attributes’, in that these all describe the motivations and ideologies behind the character’s actions. Within the superhero genre, especially for super-villains, motivations are often spoken aloud. Examples Doctor Doom’s motivations include wanting to rule the world, to humiliate Reed Richards, and to be avenged for the death of his parents.

Authorship Components This final grouping of components is slightly set apart from the others as it is to do with the non-fictional world, although still expressed via information in the texts as it concerns the people or organisations that are listed as the creators of these texts.

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Market Authors This comes directly from Freeman’s use of Foucault’s indicative function, referring to named authors who are used to guide the reader, listener or viewer’s expectations of what they will find within the text [33]. For Spider-Man in comics this would tend to mean the authors or corporate owners identified on the cover or story title, such as ‘Marvel Comics’ or ‘Stan Lee Presents’, whereas for other media it would include CBS (for the 1970s television show) and Grantray-Lawrence Animation, for various cartoons. Examples In movies, actors such as Tobey Maguire, Andrew Garfield and Tom Holland are market authors, as are directors if named on promotional material, alongside studios such as Sony and Disney.

Textual Authors Again, this is sourced directly from Freeman’s definition, and includes all those creators who are credited within the text itself. Within a comic this would generally appear in the creator credits, while a similar list of names would appear in the end credits of cartoons and radio shows. Unlike data for other components, it would in theory be possible to glean much of this information from one of the comics databases which were used as building blocks for the corpus. However, close reading of the texts showed that this data was not always accurate, and often included additional information sourced from outside the original material. This was especially true for the earlier comics texts, where the fans who entered the data used their own knowledge to list contributors who were not included in the original credits, or to give real names where pseudonyms were used. In order to maintain uniformity of data entry, only the names listed in credits were used, and so these had to be entered by hand throughout. Similarly for other types of text, data on textual authors was gathered from the published credits, such as the end titles of cartoons, rather than elsewhere. Examples Captain America’s textual authors include Joe Simon, Jack Kirby, Stan Lee, Roger Stern, Mike Zeck and many more.

Summary The unified catalogue of transmedia character components brings together aspects of the models devised by Pearson and Uricchio, Klastrup and Tosca, Marie-Laurie Ryan, Paolo Bertetti and Matthew Freeman within a framework based on Jan-Noël Thon’s ideas of Transmedia Character Networks that extends Henry Jenkins’ formulation of ‘transmedia’ in line with Scolari, Bertetti and Freeman’s Transmedia Archaeology. Where gaps were identified

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within these definitions, specifically around the area of ‘behaviour’, additional definitions were brought in using the psycholexical approach, the Big Five Index, and the idea of character motivations from creative writing practice. Where necessary the components were re-named for clarity’s sake, and finally were placed into groups based on Matthew Freeman’s classification of transmedia, with ‘behaviour’ extracted into a group of its own. This created a unified catalogue of transmedia character components containing thirteen components within four groupings, as follows: Character-Specific Components • • • •

Appearance Names and Titles Physical Actions Dialogue

Storyworld-Specific Components • • • •

Locations Other Characters Objects Previous Events

Behavioural Components • Perceived Behaviour • Personality Traits • Motivations Authorship Components • Market Authors • Textual Authors The relationship between these dimensions and their original sources is shown in Fig. 2.5. In theory this catalogue can be used as a tool for mapping the coherence of transmedia characters as they move across time and media. Used across a sample of texts, and by recording the signifiers within each component for each text, it should be possible not only to identify a character’s core components across time, but also to see whether they vary across different media or storyworlds. The next chapter will address how this tool can be used in practice, starting with the specification of a case study to test it on.

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Fig. 2.5 Relationship between dimensions and sources in unified catalogue of transmedia character components

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113. Hibbett, Mark. 2020. “Doctor Doom Survey: Results and Survey Forms.” https://doi.org/10.25441/arts.12683753.v1: University of the Arts, London, July 21. https://doi.org/10.25441/arts.12683753.v1. 114. Allport, GW, and HS Odbert. 1936. “Trait Names: A Psycholexical Study.” Psychological Monographs 47: 211. 115. Ashton, Michael C, Lee Kibeom, and Lewis R Goldberg. 2004. “A Hierarchical Analysis of 1,710 English Personality-Descriptive Adjectives.” Journal of Personality and Social Psychology 87 (5): 707–721. 116. Stangor, Charles, and Jennifer Walinga. 2014. Introduction to Psychology. 1st Canadian Edition, 526. Victoria, BC: BC Campus. 117. Fischer, Ronald, Johannes Alfons Karl, Markus Luczak-Roesch, Velichko H Fetvadjiev, and Adam Grener. 2020. “Tracing Personality Structure in Narratives: A Computational Bottom-Up Approach.” European Journal of Personality (Special Issue). doi:https://doi.org/10.1002/per.2270. 118. Hotho, A, A Nürnberger, and G Paaß. 2005. “A Brief Survey of Text Mining.” Ldv Forum 20 (1): 19–62, p. 19. 119. de Radd, Boele. 2009. “Ancient Personality: Trait Attributions to Characters in Homer’s Iliad.” Ancient Narrative. 120. Robinson, JM. 1985. “Style and Personality in the Literary Work.” The Philosophical Review (94): 227–247. 121. Ryan, Marie-Laure. 1991. Possible Worlds, Artificial Intelligence, and Narrative Theory. Bloomington: Indiana University Press. 122. Grant, Steven, Win Mortimer, and Mike Esposito. 1981. “Doctor Doom Meets Prince Namor!” Spidey Super Stories (Marvel Comics) 1 (53). 123. John, OP, LP Naumann, and CJ Soto. 2008. “Paradigm Shift to the Integrative Big-Five Trait Taxonomy: History, Measurement, and Conceptual Issues.” In Handbook of Personality: Theory and Research, edited by OP John, RW Robins and LA Pervin, 114–158. New York, NY: Guilford Press. 124. John, OP, EM Donahue, and RL Kentle. 1991. The Big Five Inventory— Versions 4a and 54. Berkeley, CA: University of California, Berkeley, Institute of Personality and Social Research. 125. Benet-Martinez, V, and O P John. 1998. “Los Cinco Grandes Across Cultures and Ethnic Groups: Multitrait Multimethod Analyses of the Big Five in Spanish and English.” Journal of Personality and Social Psychology (75): 729–750. 126. Raad, Boele De. 2000. The Big Five Personality Factors: The Psycholexical Approach to Personality. Kirkland, WA: Hogrefe & Huber. 127. de Raad, B, and M Perugini. 2002. Big Five Assessment. Boston, MA: Hogrefe & Huber Publishers. 128. Matthews, Gerald, Ian J Deary, and Martha C Whiteman. 2003. Personality Traits. Cambridge: Cambridge University Press. 129. Diener, E, and RE Lucas. 2020. “Personality Traits.” In Noba Textbook Series: Psychology, by R Biswas-Diener and E Diener, Retrieved from http://noba.to/ 96u8ecgw. Champaign, IL: DEF Publishers. 130. Norman, WT. 1963. “Toward an Adequate Taxonomy of Personality Attributes: Replicated Factor Structure in Peer Nomination Personality Ratings.” Journal of Abnormal and Social Psychology (66): 574–583. 131. Ashton, Michael C, and Lee Kibeom. 2007. “Empirical, Theoretical, and Practical Advantages of the HEXACO Model of Personality Structure.” Personality and Social Psychology Review 11 (2): 150–166.

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132. Flekova, Lucie, and Iryna Gurevych. 2015. “Personality Profiling of Fictional Characters Using Sense-Level Links.” In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing 1805–1816. 133. Goldberg, Lewis R. 1992. “The Development of Markers for the Big-Five Factor Structure.” Psychological Assessment 4 (1): 26–42. 134. Rammstedt, B, and OP John. 2007. “Measuring Personality in One Minute or Less: A 10 Item Short Version of the Big Five Inventory in English and German.” Journal of Research in Personality (41): 203–212. 135. Goldberg, Lewis R. 1999. “A Broad-Bandwidth, Public Domain, Personality Inventory Measuring the Lower-Level Facets of Several Five-Factor Models.” In Personality Psychology in Europe, Vol. 7, edited by I Mervielde, I Deary, F. De Fruyt, and F. Ostendor, 7–28. Tilburg, The Netherlands: Tilburg University Press. 136. Lang, FR, D John, O Ludtke, J Schupp, and GG Wagner. 2011. “Short Assessment of the Big Five: Robust Across Survey Methods Except Telephone Interviewing.” Behavior Research Methods (43): 548–567. 137. 157. Field, Syd. 1979. Screenplay: The Foundations of Screenwriting. New York: Dell Publishing Company. 138. 158. Hutzler, Laurie H. 2009. Character Type Overview: Creating Characters Who Are Authentic, Original, Compelling, 8. Santa Monica, CA: Laurie Story Inc. 139. Lee, Stan, Jack Kirby, and Joe Sinnott. 1967. “Doomsday.” Fantastic Four (Marvel Comics) 1 (59). 140. Hutzler, Laurie. 2009. The Character Map. 141. Lee, Stan, and Steve Ditko. 1963. “Spider-Man.” The Amazing Spider-Man (Marvel Comics) 1 (1). 142. Foucault, Michel. 1969. “What Is an author?” In The Art of Art History: A Critical Anthology, by D Preziosi, 321–334. Oxford: Oxford University Press. 143. Freeman, Matthew. 2015. “Author-as-Franchise-Product: Edgar Rice Burroughs Inc and Tarzan as Historical Branded Entertainment.” In Engaging Consumers through Branded Entertainment and Convergent Media, edited by José MartiParreño, Carla Ruiz and LL Scribner, 53–73. Pennsylvania: IGI Global. 144. Claremont, Chris, Dave Cockrum, and Joe Rubinstein. 1981. “Kidnapped!” The Uncanny X-Men (Marvel Comics) 1 (145). 145. Scolari, Carlos, Paolo Bertetti, and Matthew Freeman. 2014. Transmedia Archaeology: Storytelling in the Borderlines of Science Fiction, Comics and Pulp Magazines, 17. Basingstoke: Palgrave Macmillan UK. 146. Moench, Doug, and Tom Sutton. 1980. “The Power of the People!” Fantastic Four Annual (Marvel Comics) 1 (15). 147. Conway, Gerry, George Perez, and Pablo Marcos. 1977. “To Stand Alone!” The Avengers (Marvel Comics) 1 (155). 148. 2021. Spider-Man: No Way Home. Directed by Jon Watts. Produced by Colombia Pictures.

CHAPTER 3

The Corpus and Sample

This chapter will discuss how and why Doctor Doom was chosen as the case study for this project, how a corpus of texts was decided upon for analysis, and then how this corpus was reduced to a manageable but still representative sample for data collection and entry.

Choosing Doom Doctor Doom is a super-villain, something which was made clear in his very first appearance on the cover of Fantastic Four #5 [1] where he threatens to ‘destroy the Fantastic Four forever’ in a story entitled ‘Meet Doctor Doom!’. Super-villains have been around almost as long as superheroes, created as worthy antagonists who would maintain the heroism of the superhero’s story, rather than showing them beating up much less powerful adversaries like the bank robbers or muggers inherited from pulp storytelling [2, 3]. Super-villains also came to fulfil the storytelling function of threatening change, allowing the superhero to remain the protector of the status quo while still being involved in the story [4]. Doctor Doom was originally created in opposition to the Fantastic Four, as ‘the intellectual equal of Reed Richards’ [5]. He was born Victor von Doom, the only son of a poor gypsy family in the small Eastern European country of Latveria, where he was raised by his father after his mother was murdered for being a witch. After his father died fleeing from the evil local Baron the young Victor became a scientific Robin Hood figure, battling the aristocracy with ingenious inventions like protective coatings for gypsy caravans that could stop tank shells. This brought him to the attention of the American establishment, who spirited him away to University where his isolation and perceived © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 M. Hibbett, Data and Doctor Doom, Palgrave Studies in Comics and Graphic Novels, https://doi.org/10.1007/978-3-031-45173-7_3

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arrogance led to a devastating accident and horrific facial injuries. Doom journeyed to Nepal in search of a cure, where he discovered an order of monks who helped him to embrace the mystic arts of his mother and accept his new identity. He eventually encased himself in armour and declared ‘From this moment on there is no Victor von Doom… from this moment on I shall be known as Doctor Doom!’ [6]. From here he returned to Latveria, overthrew the monarchy, and installed himself as the nation’s beloved ruler. In many ways this story is like a superhero origin, with a rich backstory giving wide scope for future adventures, an exciting visual identity, and complex, sympathetic motivations for even his most evil deeds. The difference that makes it a super-villain origin is that Doom sought to change the status quo rather than maintain it. This portrayal of Doom changed subtly over the years, echoing the changes of American attitudes during the cold war, so that he would fluctuate between being a deranged dictator out to destroy all that was good, and a sympathetic revolutionary leader who just happened to have a different point of view to the US heroes [7]. However, while this portrayal did change over time, Doom always retained elements of the super-villain, most notably the facial disfigurement—his ‘wound’—which he blamed on Reed Richard’s supposed interference with his experiments [8]. His costume was also resolutely villainous, with ominous mask and hooded cloak, coloured green, grey and brown with a hint of gold to signify his regality, but for the most part resembling a ‘great, clanking cyborg who… peers through eyeholes like the archers’ slits in a medieval castle, and who appears to be literally sweating rivets’ [9]. Figure 3.1 shows Doctor Doom as he appeared throughout most of ‘The Marvel Age’. Doctor Doom also had roots in the traditional ‘mad scientist’ character, through his appearance, general activities and notably the ‘forbidden experiments’ [1] which caused his disfigurement. Mad scientists regularly appeared in the Hollywood movies of Stan Lee and Jack Kirby’s youth, reflecting threats to society from new ‘ungodly’ knowledge [3]. Indeed, Doom’s birth name of Victor echoes that of the first science fiction mad scientist, Victor Frankenstein, also referred to as ‘Victor von Frankenstein’ in many later adaptations [3, 11]. Additionally, Peter Coogan places him in the category of ‘the enemy commander’, someone who has the resources of a state behind him and is in a position of legal authority within that society and is ‘ideologically motivated to conquer or subvert the nations and societies they are at war with’ [12]. This dual motivation, to gain knowledge while simultaneously conquering the world, is reflected in Doom’s typically super-villain-like belief that the world would be a better place if only he was in charge—even if that better world would see humanity enslaved under his rule [13, 14]. He believes that nobody else has the intellectual or moral stamina to achieve this, and thus that all of his actions are justified in pursuit of what he sees as a future utopia

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Fig. 3.1 Doctor Doom as seen in The Official Handbook of the Marvel Universe #3 [10]

[15, 16]. It is this self-justifying yet theoretically moral worldview that makes Doom a complex, often sympathetic, character who remains, all things taken into account, a super-villain [17, 18]. According to Stan Lee, Doctor Doom was an instant hit with the readers. ‘Within a matter of days the mail came flying in. And it all carried the same message. Bring back Dr. Doom!… After the first thousand or so letters we suspected we had a hit! So bring him back we did’ [19]. Doom’s popularity was such that, as the Marvel Universe started to develop into a single storyworld, he began to appear in other series. Just over a year after his first appearance he guest-starred in The Amazing Spider-Man #5 [20] and over the course of his first decade also appeared in Daredevil , The Avengers , Strange Tales , The Silver Surfer, Sub-Mariner, Thor, Captain America, Iron Man, The Incredible Hulk and Not Brand Echh, as well as a brief run as the second feature in Astonishing Tales . Doom’s wanderings were not, however, restricted to comics. Marvel began to tell their stories on multiple media platforms early on in their history, with the cheaply made The Marvel Superheroes [21] being the first [22]. This was

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followed by Hanna-Barbera’s Fantastic Four cartoon series [23] cartoon series the following year. Doom appeared in several episodes, all of which were simplified retellings of stories from the early issues of the comic. This series was significantly more professionally produced than The Marvel Superheroes , but was still clearly not set in the same storyworld as that of the comics, with most episodes featuring retellings of stories taken from the early issues of the comics series, often simplified and, in some cases, corrected so that the plots made (slightly) more sense. During the 1970s and 1980s Doom continued to be a wandering presence in Marvel comics, drafted in as villain for prestigious special issues such as Marvel Comics Super Special #1 [24] featuring the band Kiss, and the second Superman/Spider-Man team-up in Marvel Treasury Edition#28 [25]. As time went by he roamed ever more widely across the storyworld, guesting in almost every series that Marvel published, as well as enjoying a brief run sharing the bi-monthly title Super-Villain Team-Up with Namor The Sub-Mariner from 1975 to 1978. Doom also continued to appear in a wide variety of other media formats, including The Fantastic Four Radio Show [26], the Amazing Spider-Man newspaper strip [27], the Spidey Super Stories Album [28], The New Fantastic Four cartoon series [29], the novel Doomsday [30] and the Spider-Man cartoon series [31]. He would go on to appear in other media for the rest of the century and beyond, including multiple video games and the two live action films directed by Tim Story, The Fantastic Four [32] and Rise Of The Silver Surfer [33], and his most recent live action outing, Fantastic 4 [34]. There was even very nearly a solo Doctor Doom movie written by Noah Hawley which apparently adapted his origin story, although this never made it into production, and new rumours about his arrival in the Marvel Cinematic Universe swirl every time a new movie is released [35]. Thus, Doctor Doom was chosen as the case study for this project because the nature of his appearances meant that he would function as a way of sampling the different Marvel storyworlds over time. The fact that he rarely had his own series and so did not have a specific editor or group of textual authors who ‘owned’ him meant that many different authors and texts could freely use him as a plot-engine, dropped into an existing series as a device to create a story. Although he was initially introduced as an enemy of The Fantastic Four he was very quickly shown to be suitable as an adversary for any hero, and the analysis shows that he was soon used across both comics and non-comics texts in a way that other super-villains who were more closely tied to specific superheroes, such as The Red Skull or Loki, were not. By following Doom’s adventures over time one can thus find a route through the history of the ongoing Marvel storyworlds.

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Inclusion and Exclusion Criteria With the case study chosen the next stage was to define a corpus to examine, and this required inclusion and exclusion criteria. Inclusion criteria are ‘the key features of the target population that the investigators will use to answer their research question’, which in this context might include specific types of text, dates of publication or geographical location of retailers [32]. Exclusion criteria refer to people or texts ‘which meet the inclusion criteria but present with additional characteristics that could interfere with the success of the study’, which for studies of texts might include those that are unavailable or impractical for study, such as a rare printing that has not been digitised [36]. Setting clear inclusion and exclusion criteria is a way to make study results reproducible. Being clear about what is to be included in a corpus or not means that future researchers can re-use the resulting data with confidence, and compare it to other similarly constructed studies to get richer results. Examples throughout this section will be drawn from the study of Doctor Doom as a transmedia character. Inclusion Criteria The inclusion criteria for this study were as follows: 1. The text included a version of Doctor Doom The corpus attempts to capture appearances by the character Doctor Doom, and so all appearances by him were included, even those where he only appeared indirectly, such as on a television screen as in Fantastic Four #18 [37] or in a gallery of statues in Fantastic Four #10 [38]. 2. The text featured a narrative The study was looking at Doctor Doom as a transmedia character, i.e. one based in a narrative storyworld. Thus the corpus should, by its nature, include only narrative-based media, such as comics, books, television series and radio shows. Items without a pre-defined narrative, such as toys, board games, and other merchandise for Doctor Doom do exist, and would be a fascinating alternative way to chart his development as a character, but do not form part of this research. 3. It was published or otherwise issued during ‘The Marvel Age’ If the criteria were simply ‘any narrative text featuring Doctor Doom’ then the corpus would need to be added to, and all analyses re-run, every time a

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new comic, TV show, movie or any other possible future media format was released featuring the character. Thus start and end points needed to be set, and defined in such a way that the same analysis could be re-done on the same selection of texts with the same results. Additionally, these start and end dates needed to be justified in some way in order to avoid introducing personal preferences that might skew results, for example by stopping selection before texts appeared that were difficult to analyse, or that did not fit in with a desired result. Hence the so-called ‘Marvel Age’ was used as the period to be investigated. This is a term regularly used in comics fandom, biographies, popular texts, journal articles and academic volumes, as well as in Marvel’s own publicity, to describe a period that began with the publication of Fantastic Four #1 [39, 40], seen as the beginning of the ‘Marvel Universe’ storyworld which would come to dominate American superhero comics over the next several decades [41]. This start date is almost uniformly agreed on, but the end date is much more vague, usually thought to be some point in the mid to late 1980s when DC took over as the home of innovation in superhero comics and Marvel was recast as the conservative sales-leader [42, 43]. However, a specific end date was required for the generation of an empirical dataset, and so the Marvel Age was periodised using the production of culture approach [44]. This periodisation primarily used the position of Editor-inChief as a marker for a change from one period to another, so that the Marvel Age can be said to begin with comics dated November 1961, when Fantastic Four #1 under the editorship of Stan Lee, and end with those dated October 1987, the final month in which all Marvel comics listed Shooter as Editor-inChief. The editor-in-chief position was further used to divide the Marvel Age into three sub-periods, which were given new descriptive names for ease of reference. The first was the ‘Creation’ period contained within Stan Lee’s time in charge, with comics dated November 1961 to August 1972. During this period Marvel’s small, quick-footed structure allowed it to experiment and develop in exciting new ways that its much bigger rival DC was unable to compete with, and it was here that much of the Marvel Universe that we know today was created. This was followed by the ‘Chaos’ period from September 1972 to April 1978. Here the commercial success engendered by the success of the preceding period led to the company’s sale to Cadence and then further sales success in the following decade which, in turn, led to internal chaos as Marvel’s output and staffing grew quicker than its organisational structure could adapt to deal with. This turmoil was exemplified by the fact that there were five different Editors-in-Chief during the six years of the ‘Chaos’ period—Roy Thomas, Len Wein, Marv Wolfman, Gerry Conway and Archie Goodwin. Finally, the ‘Consolidation’ period from May 1978 to October 1987 saw Jim Shooter brought in to implement organisational reforms which professionalised the running of the company. This allowed it to develop deals with other

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companies, licensing out its own characters in other media and taking on the licensing of other companies’ characters for comics adaptation. However, this professionalisation reduced its ability to experiment and develop and, ironically, allowed its now smaller rival DC to take on the creative, underdog role that had been the source of Marvel’s initial success, and so brought about the end of the Marvel Age [44]. The dates used for this periodisation are defined using the ‘cover dates’ of the comics texts (the dates shown on the covers) which listed the individuals as editor-in-chief. This method was used because the actual release dates varied regionally and were not generally recorded, whereas the cover date was not only available on the front of the texts themselves but is included within all of the databases used to generate the corpus. These cover dates would generally be about three months ahead of the intended on-sale date, in theory to give the publication a longer shelf-life before the news vendor removed it from sale [45, 46]. However, distribution was not even across the country, and so comics would have actually gone on sale on different dates across the USA. Similarly, the date of first publication or broadcast for other media items is often imprecise, and so in order to make inclusion dates for such items as close as possible to those for comics texts they were allocated ‘cover dates’ that related to the month in which they were first issued. So, for example, the episode of The Marvel Super Heroes ‘Doctor Doom’s Day’ [21] was first broadcast on 25 November 1966 and so for database purposes was given a ‘cover date’ of February 1967, three months later [47]. For the corpus as a whole, this meant that any text that was first published, broadcast or otherwise issued from August 1961 to July 1987—relating to cover dates from November 1961 to October 1987—was included [48, 49]. The final definitions of ‘The Marvel Age’ and its sub-periods are shown in Table 3.1. Defining the end of ‘The Marvel Age’ in this way, with comics dated October 1987, complies with the idea expressed (however vaguely) that it ended at some point in the mid- to late 1980s, notably with Steve Englehart’s suggestion that it happened during his run as writer on Fantastic Four from 1987 to 1989 [50]. Table 3.1 Definition of ‘The Marvel Age’ and its sub-periods

Cover date Sub-Period

First

Last

Creation Chaos Consolidation The Marvel Age

Nov 1961 Sep 1972 May 1978 Nov 1961

Aug 1972 Apr 1978 Oct 1987 Oct 1987

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Exclusion Criteria The exclusion criteria were as follows: 1. The text was not a paratext Paratexts are aspects of a text that exist aside from the main narrative texts such as advertisements and letters pages [51]. A few of these included a narrative although most did not and, in all cases, consumers of the main text would recognise these as not part of the main story [52]. The main reason to exclude these items, however, was that their inclusion would have made data collection almost impossible to complete with any accuracy, as they are only very rarely recorded within existing databases, and so the only way to reliably track them all down would have been to read physical copies of every comic published during this period. This is a shame in many ways, as Doom’s appearances in paratexts are often fascinating, but here they were excluded as otherwise they would ‘interfere with the success of the study’ [36]. 2. The text was not unofficial An analysis of unofficial versions of Doom, such as fanfiction and fanzines, would offer an insight into the way the character is viewed and re-used by consumers of the character, and such activities are the focus of much transmedia research [53]. However, such texts were excluded for two reasons. Firstly, it would be difficult if not impossible to track down and examine all fan-made texts, especially any created during Doom’s earliest years, and secondly such texts would not be part of, or have any relation to, the existing storyworlds. As with limitations imposed by other criteria, this excludes fascinating material worthy of analysis, but doing so allows for a practical corpus to be created that can be used to test the reliability of the unified catalogue of transmedia character components. It should also be noted here that versions of the character that appeared in cartoons and other media, such as The Marvel Superheroes series, are considered ‘official’ where they are licensed by Marvel to other companies. 3. It was not a reprint Marvel published a range of reprint series during the 1970s, such as World’s Greatest Comics (reprinting The Fantastic Four) and Marvel Tales (reprinting The Amazing Spider-Man). Collected editions of comics, such as Origins of Marvel Comics, were rare at this point and, with the growing importance of continuity within the Marvel Universe, these series gave new readers an opportunity to catch up with older stories [54, 55] These series did not,

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however, include any new narrative material, and so were not included within the corpus. Similarly, foreign editions were excluded, as these were almost always reprints. Occasionally some would make amendments to the reprinted stories—the publishers of Fantastic in the UK, for example, would make changes to costumes in order to maintain the continuity of their own, slightly different, publishing timeline—but they were otherwise reprints and so are excluded [56].

Identifying the Corpus With inclusion and exclusion criteria set, the next step was to use them to assemble a corpus. For the purposes of this research data sources were divided into two main types of text: comics and other media. The bulk of Doctor Doom’s appearances were in comics, which have a fairly uniform categorisation system of title, date and number, so it made sense to group these together and find ways to access data about them first before looking at the more varied, though in this case less numerous, appearances in other media.

Sources for Comics Texts An obvious option for selecting comics texts would be to simply read every Marvel comic published during this period and identify all those in which Doom appeared. However, while reading through a quarter century of Marvel Comics might be enjoyable, it would be immensely time-consuming, especially when there are other much simpler ways to find such texts. Several existing catalogues of Doom’s appearances were found online but these had many omissions. One of the most complete lists built by fans was The Latverian Embassy which contained a chronological list of comics in which he featured, but when comparing its listings to other reference sites (detailed below) it was found to have missed 28% of Doctor Doom’s appearances in the 1960s alone [57]. Another example was The Marvel Chronology Project , which was based on a mission to place every story from Marvel comics into an in-universe chronological order [58]. For example, it put the first issue of The Books Of Doom [59] first in a list of Doom appearances, interspersed with flashbacks to sections from other stories such as Fantastic Four Annual #2 [6] and Marvel Superheroes #20 [60]. Unfortunately The Marvel Chronology Project did not allow access to its underlying data, presenting only static reports with the option to email the site owners to request specific, limited, additional information. Some other research projects have used The Marvel Chronology Project in this way but the lack of accessibility to the main dataset, and the absence of open data about publication dates, severely limited its usefulness here [61].

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Two podcast series exist that seek to analyse all of Doom’s appearances. The Doctor Doom Cast put together its list of texts using The Marvel Chronology Project , whereas Douglas Wolk read every single Marvel comic before selecting texts for Voice Of Latveria [62, 63]. However, both podcasts omitted appearances where Doom does not appear ‘in person’ (e.g. background portraits, statues or TV screens) and exercised other value judgements, such as not covering issues where Doom appeared very briefly. More importantly than these problems, both series ended after about a year of broadcasting, and so did not cover anywhere near the full Marvel Age. Looking beyond such projects there are several currently active communitybased online databases which are dedicated to recording information about the history of comics publications, particularly those issued by American publishers, and these do tend to allow direct access to their data, so that simple queries can be used to extract customised datasets. These databases also use online data entry forms that allow anybody to suggest changes or updates to the data, although they are not completely ‘open’ systems and still require moderator approval before going live. This ‘peer review’ of the data makes them, in theory at least, more comprehensive and reliable than those set up by single enthusiasts, although, as will be seen, different biases are still present. Of the four databases consulted for this project the longest running was The Grand Comics Database, which was set up in 1994 as a successor to the paper-based Amateur Press Alliance for Indexing [64, 65]. The Grand Comics Database has been used by other researchers in Comics Studies due to the ease of access to its data [66–69]. It can be downloaded as a complete database, making it a much more flexible tool than its competitors which allow querying only via API (Application Programming Interface), a means of giving users the ability to pass simple queries to the online database and receive datasets in a format which can then be used to display customised information [70]. Other databases which take a similar approach are The Comic Book Database, Comic Vine and The Marvel Database [71–73]. The Comic Book Database has been described as cataloguing ‘every comic book, graphic novel, manga, illustrator, publisher, writer, and character … ever’ [74]. It claims to be ‘the largest database of its kind’, while Comic Vine similarly calls itself ‘the largest comic database online’ [72]. Each of these three databases requires registration to allow queries and edits, with access to data available via API. Setting up API queries proved to be very complicated, and only allowed a set number of data interrogations, so for the purposes of this research data scraping was used instead. This is a simplified, if laborious, way of extracting data which uses human interaction, such as copying information from a webpage into a text editor and then manipulating it into a format whereby it can be used in a database, to get information from websites [75]. In each of these cases the data extracted was less rich and less adaptable than that which was available by uploading the Grand Comic Database SQL file to a personal server. Other researchers with more skills using API systems might, of course, have fared better.

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With all this in mind the eventual strategy for creating a list of comics texts featuring Doctor Doom was to use the Grand Comics Database to generate an initial corpus which could then be checked against the less rich datasets scraped from The Comic Book Database, Marvel Database and (to a much lesser extent) Marvel Chronology Project , with Comic Vine used as a tool for manually checking individual cases. In this way I hoped to avoid the biases inherent in using the results from any single community while also increasing the likelihood of capturing as many appearances as possible. Querying the Grand Comics Database produced a list of 243 comics which supposedly featured Doctor Doom. The next stage was to check this against Comic Book Database and Marvel Database as detailed above, but in order for that to happen a great deal of data cleaning was first required. ‘Data Cleaning’ is a process whereby datasets are ‘cleaned’ of any errors and coding schemes are made uniform to enable linkage and analysis [76]. In this case the coding schemes needed to be made uniform first, in order to be able to check the databases against each other for errors, which turned out to be a lengthy task. The long-running, community-based nature of the different databases meant that there was very little uniformity about data entry within each system, and even less when trying to link them together. One example of this was the recording of the definite article in series titles. For example, some databases would refer to the series The Fantastic Four as The Fantastic Four, while others referred to it as just Fantastic Four. Similarly when series changed name over time, such as The X-Men becoming The Uncanny X-Men and then New X-Men, each dataset dealt with this in a different way. Understanding that these refer to the same series would not be a problem for a human being reading through each item one by one, but it poses difficulties for a computer system trying to match text strings—to a simple text query ‘X-Men’ is not the same as ‘The X-Men’ and neither is the same as ‘New X-Men’, ‘The New X-Men’ or ‘The Uncanny X-Men’. These issues can be resolved by using more sophisticated queries, but other problems were more difficult, such as how different community members dealt with the use of hyphens, volume numbers, how to catalogue special editions and annuals, and so on. Such difficulties are common to all comics databases, and have caused problems for other quantitative data analyses of comics, with the solution always being a manual check of the individual data items [69, 77]. The policy when recoding data within the various datasets was to, wherever possible, use the naming and coding conventions of the Grand Comics Database as a guide, and where different systems disagreed about the ‘truth’ of an entry to use Comics Vine as an ‘independent adjudicator’. This process took a considerable amount of time but when it was finished the three databases could be linked together and searched for appearances of Doctor Doom. This search generated further problems, notably around the occasional inclusion of paratexts, such as in the case of Journey Into Mystery #125 [78]. According to The Grand Comics Database (though none of the other databases) this text features an appearance by Doctor Doom, but when the text

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was checked he was nowhere to be seen. This came about because of the use of Marvel Unlimited for most of the close reading [79]. This is an App which gives subscribers access to over 20,000 digital comics scanned from Marvel’s archives, a fantastic resource with one significant limitation in that it (generally) only contains story pages. Very occasionally letters pages are included, but never any other paratexts such as adverts or additional editorial. Thus if the original data entry was based on a reading of a physical copy of the original comic then paratexts might be included, but if the reading was based on a reprinted trade paperback or digital copy then it would not. In the case of Journey Into Mystery #125 a scanned copy of the complete original comic was found online, which revealed that Doom had appeared in an advert for an Incredible Hulk Sweat Shirt. This was Doom’s first use in advertising, as far as I was aware, and also his first appearance written by somebody other than Stan Lee, as further research showed that it had been both written and drawn by Marie Severin. However, as discussed earlier, the fact that this was a paratext meant that it fell foul of the exclusion criteria and was sadly not included in the corpus. There was also the problem of over-reporting, which occurred on several occasions where Doom was listed as appearing in a comic but did not really appear at all. For example, The Grand Comics Database listed him as appearing in issues 65, 85, 90 and 91 of The Defenders , but he does not appear in any of them. On this occasion I was able to access the original comics in the UAL Archives and Special Collections and discovered that in each of these Doom is briefly mentioned in the ‘Bullpen Bulletins’ editorial page, common in all Marvel comics published that month, along with several other characters. To be clear, this was not an appearance at all, either in the narrative or paratexts, and similar mentions occurred many times across the time period without being recorded anywhere. My best guess as to the cause of this is that whoever first did the data entry for The Defenders was determined to be as thorough as possible. This lack of consistency caused further problems when trying to use the databases to compare Doom with other characters. During the early stages of this project the graph shown in Fig. 3.2 was generated using raw data from the Grand Comics Database, showing Doom’s appearances across the Marvel storyworld compared to other super-villain characters. This seemed to show that The Red Skull appeared in almost every single Marvel comic in 1976. Further research showed that this was due to an advert for Hostess Twinkies that appeared in comics cover-dated July or August of that year [80]. Adverts such as these appeared on a regular basis, with the same strip being featured in most comics published in a given month, but usually these were not added to any of the databases. In this case it seems that somebody was determined to do so for every single text, skewing the resulting data massively and showing, once again, the importance of thoroughly cleaning the data before attempting to do any quantitative analysis on it.

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40 35 30 25 20 15 10 5 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987

0

Doctor Doom

Loki

Mephisto

Red Skull

Fig. 3.2 Number of different series for leading super-villains, by year

This data cleaning generally resulted in appearances being removed from the listings, although linking the databases together did reveal additional texts. When the Grand Comics Database data was first linked to the data scraped from the other databases it appeared that there were 22 additional texts featuring Doctor Doom, but on closer examination 19 of these were either mistakes, where Doom did not appear at all, or incorrectly listed reprints. The remaining three cases definitely featured Doctor Doom though, including a very enjoyable guest appearance in Marvel Comics Super Special [24] starring the band Kiss, so these were added to the first draft of the corpus. These examples highlight the importance of using multiple data sources to spot errors, augmented with close reading to double check. Close reading was also vital for the detection of additional texts that did not appear in any of the databases used. For example, while reading Master Of Kung Fu #60 [81] I noticed that a lot of the story made no sense, as it seemed to rely on Doctor Doom having already appeared in the series, despite not being listed as doing so in any database. On checking the previous instalment Master Of Kung Fu #59 [82] I found that Doom did indeed appear, in a shock final page twist. This omission might, therefore, have been due to the individual responsible for data entry not wanting to spoil the twist, in which case it seems likely that other databases had simply copied this data for their own systems later. Initial thoughts from these close readings were recorded on the Marvel Age Doom blog. Doing this for the whole corpus allowed for what Franco Moretti calls a ‘distant reading’, where stepping back from individual texts allows one to ‘focus on units that are much smaller or much larger than the text: devices, themes, tropes - or genres and systems’ [83]. By aggregating and analysing large amounts of data in this way, Moretti says, one can uncover insights into the broader literature that are impossible to find by simply focusing on a specific canon of texts [84]. This was certainly the case over the four years it took to complete the close reading of the entire corpus, with many tropes

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emerging that might otherwise have been missed if the research had relied entirely on the databases. One particularly rich source of information, for example, came from the series Not Brand Echh. Only one issue of this series appeared in the initial version of the corpus, but reading this text made me suspicious that he might appear in others that were not listed in any of the databases, and checking revealed an additional nine texts that Doom appeared in, several of which featured him on the cover. Other Types of Text Searching for other media appearances by Doctor Doom was a much more ad hoc process than for comics texts, as there was no single database containing all media outputs with a list of characters who appeared in them. Some functionality is available within IMDb (Internet Movie Database) but this does not currently include the ability to search by character name, and so the Google search engine was used as the starting point for this part of the corpus. Searches were run on Google using a variety of terms combining the variant versions of the character’s name (‘Doctor Doom’, ‘Dr Doom’, ‘Dr. Doom’ and ‘Victor Von Doom’) with words describing media types, such as ‘television’, ‘radio’, ‘film’ and so forth. Similar searches were run using ‘Fantastic Four’, as any media that featured them might possibly feature their arch enemy. In this way a large number of texts were discovered, from the obvious—such as the two Fantastic Four cartoon series [23, 29]—to the obscure, including the Fantastic Four Radio Show [26] starring a young Bill Murray as The Human Torch. Finding other items involved reading around the texts that had already been identified. Reading a blog about Doom’s appearance in the Marvel Superheroes cartoon [21] uncovered a casual mention of a live action segment featuring the character created by WNAC-TV in Boston, Massachusetts to promote the series [85]. Sadly no other evidence of this remains so it was impossible to include in the corpus, but other items were easier to find. The Power Records album The Fantastic Four: ‘The Way It Began’ [86] was found by accident, appearing in search results while looking for information about the similarly titled 1967 cartoon episode The Way It All Began [23], but once identified it was straightforward to track down a recording on YouTube. Though less straightforward and methodical than the collection of the comics corpus, the search for other narrative media revealed many more transmedia texts featuring Doom than initially expected, in a surprising breadth and diversity of formats. Using the methods described in this section, an eventual corpus of 266 texts was identified, as shown in Appendix B. The next step was to decide which of those texts to analyse.

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Selecting a Sample Analysing the entire corpus would have been possible, though not practical. I estimated that if the time for all tasks was combined it would take around one working day to fully read, digest, analyse and enter data on all thirteen dimensions for each individual text, which meant that, allowing time off for weekends, it would have taken about a year of working flat out to analyse the entire corpus of 266 texts. This was not feasible for me, as an unfunded parttime Ph.D. student with a full-time job, and analysing a full corpus would be even less practical for future researchers examining more widely used characters who might not only star in multiple series of their own across various media but also appear as guests in other series, and so have much bigger corpuses. What was required, therefore, was a methodology which could be used by myself and others to identify a group of texts that was of a more manageable size but would still yield results that would be comparable to those gained from an examination of the corpus as a whole. In other words, what was needed was a sample. Sample Significance and Representativeness The first step in selecting a sample from a corpus is usually to determine its size [87]. This is done by working out how big the sample would need to be to yield results similar to an analysis of the corpus as a whole, while still being of a manageable size for practical data collection and analysis. One way of doing this would be through statistical significance, a statistical term referring to the likelihood that the results of an analysis are occurring by chance [88]. This is calculated by generating a ‘p-value’, standing for ‘probability’, with a low p-value indicating a low probability that the results of an analysis are due to random chance and are therefore ‘significant’, while a high p-value means the opposite [89]. This is a key tool in statistical analysis used by many studies, especially in population-based surveys where a smaller sample is used to analyse a much larger group of people. However, the proportional size of the sample depends on the size of the original corpus—a very large corpus with thousands or even millions of subjects would only require a comparatively small sample to be significant, whereas a smaller corpus would need a larger percentage in the sample. For example, some years ago I was involved in The National Evaluation Of Sure Start, a project to evaluate the effectiveness of a government childcare programme called Sure Start [90]. The evaluation involved interviews with only 0.75% of all families using the service but this was still statistically significant because the original population (equivalent in this field to a corpus) was over a million families, and so 0.75% of the population generated a sample of over 9000 families. Compared to this 266 texts is a tiny corpus, and a rudimentary check of the numbers needed to give significance came out with a result of 158 texts

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being required [91]. While this was at least slightly smaller than the overall corpus, it did not make sufficient difference to the total to make it practical, so a different method was required. This was found within the field of corpus linguistic analysis [92]. Although the use of CLA as a primary methodology had already been ruled out, its employment of ‘representativeness’ as a basis for sampling was very useful [93]. Here ‘representativeness’ refers to ‘the extent to which a sample includes the full range of variability in a population’, in this case meaning that the types of texts, the numbers of each available, and their contents should be of a similar proportion in both the corpus and the sample [94]. In other words, a representative sample is one which has the same general characteristics of the corpus as a whole, and so although results would not be ‘statistically’ significant in the formal sense they could be said to be significant in a broader sense as they reflect the larger corpus. This representativeness can be tested by comparing ‘the occurrence and/or proportion of situational characteristics represented in the full population’ to that in the sample, to assess whether the two match [95]. To paraphrase again, if it can be shown that a sample has the same properties as the overall corpus then it can be said to be ‘representative’ and therefore valid for analysis. This problem with using this approach was that testing whether the character-based properties of the sample were representative of the same properties in the corpus as a whole required foreknowledge of what these properties actually were, and as the main purpose of the analysis was to identify these very properties then the whole thing becomes a circular problem: a representative sample is required in order to discover the properties of the corpus, but you need to know the properties of the corpus in order to identify the representative sample. The answer to this problem was to use other properties of the corpus and sample which were not directly related to the characters within the texts, but rather to the texts themselves. One obvious property to examine, for instance, was the names of the series that Doctor Doom appeared in. Every narrative appearance, in whatever medium, was within a text that had a title of some kind so, in theory, these could be used to measure representativeness. For example, if the sample and the main corpus both featured the same percentage of issues of The Fantastic Four, The Avengers , Supervillain Team-Up and so on then the former could be said to be representative of the latter. The names of series had already been collected as part of the generation of the corpus, and so it was straightforward to extract a table from the database showing the titles of all the texts in which Doom had appeared. This showed that Doom appeared in 72 different series altogether, with over half of the titles (42 out of 72) only referring to one or two texts in the corpus as a whole. Attempting to making a representative sample using this data would be impossible, as the only way to ensure that the 0.37% of the texts in the sample had the title West Coast Avengers , for example, would be to have a

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sample containing all 266 texts. Thus some recoding was required to create larger groupings which would enable the generation of a sample that was still representative of the corpus. Firstly annuals and other special editions were recoded according to their ‘home’ series, so that for example Giant-Size Avengers and The Avengers Annual were placed into the same group as The Avengers , while Giant-Size Super-Villain Team-Up was grouped with Super-Villain Team-Up, and so on. Once this was done some titles were grouped together thematically, often based on their specific media. Examples included the various role-playing game companions such as Marvel Superheroes Players Book, Marvel Superheroes RolePlaying Game Judge Book, Marvel Superheroes Secret Wars RPG and so on which were grouped together as ‘RPG Magazines’, while series such as Marvel Treasury Special, Marvel Treasury Edition and Marvel Comics Super Special which did not relate to specific series were grouped as ‘Specials/Treasuries’. Finally, any remaining single issues that could not be recoded in any of the above ways were put into the general groups ‘Other comics’, ‘Other cartoons’ and ‘Other non-comics’. Once this was completed there were 34 groupings of titles, each containing at least three texts. These are shown in Table 3.2. Grouping texts in this way also removed the need to take into account the type of media that Doom appeared in. Each series title contained only one media type within it, which meant that making the sample representative of series titles would also make it representative of media type. Using these groupings did not, however, guarantee that the sample would be representative of the time periods that Doom appeared in. The series Fantastic Four, for example, runs throughout the entire period of the corpus, so simply having a representative number of texts with this title would not guarantee that it was representative of his appearances within it over time. If all of the Fantastic Four texts analysed came from the 1960s, for instance, they might give very different results from one which featured only 1970s texts, or 1980s. For this reason the sample also needed to be representative by time as well as by title. Attempting to divide the texts up by year of publication resulted in groupings that would be too small to combine with titles, so instead the three sub-periods within ‘The Marvel Age’—‘Creation’, ‘Chaos’ and ‘Consolidation’—were used. Grouping texts together in this way gave the grouping shown in Table 3.3. Thus, in order to be representative by time the sample would need to contain 29.3% of its texts from between November 1961 and August 1972, 20.3% from between September 1972 to April 1978, and 50.4% from between May 1978 and October 1987. With numbers rounded to whole numbers this resulted in the desired sample size by period shown in Table 3.4. Attempts to make the sample representative in other ways proved problematic. At first it seemed logical to make the corpus representative of the various creators who were involved in Doom’s stories. In theory this data should have been available from The Grand Comics Database, but this proved to be highly

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Table 3.2 Recoded titles of texts in which Doctor Doom appeared

Series

Texts

Fantastic Four Super-Villain Team-Up Marvel Super-Heroes Secret Wars Not Brand Echh The Avengers What If? Astonishing Tales Spider-Man/And His Amazing Friends Thor The Amazing Spider-Man Spidey Super Stories Specials/Treasuries Iron Man Daredevil RPG Magazines The Uncanny X-Men Sub-Mariner Beauty and the Beast The Incredible Hulk Dazzler Marvel Team-Up The Thing Marvel Fanfare Fantastic Four vs. X-Men Fantastic Four (radio show) Fantastic Four (1967 series) Secret Wars II Crazy Magazine Marvel Two-In-One Strange Tales The Amazing Spider-Man (newspaper strip) Other comics Other non-comics Other cartoons Total

60 16 12 10 10 9 8 8 7 6 6 5 5 5 5 5 4 4 4 4 4 4 4 4 4 4 4 3 3 3 3 27 3 3 266

Table 3.3 Corpus texts by period

Period

Texts

%

Creation Chaos Consolidation Total

78 54 134 266

29.3 20.3 50.4 100.0

3

Table 3.4 Sample texts by period and media type

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Period

Texts

%

Creation Chaos Consolidation Total

20 14 35 69

28/9 20.2 50.7 100.0

unreliable with many errors and omissions, especially for inkers and letterers. The Grand Comics Database also included information that was not available in the original texts, notably the names of colourists uncredited in earlier comics, and also ‘corrections’, where the printed information was deemed to be wrong by the users who had entered each comic’s details into the database. These problems with the reliability of data sourced from The Grand Comics Database have been noted elsewhere [69]. Furthermore, data on contributors to non-comics texts was not available in this way, and would need to be collected during the analysis period, which again led to a circular problem—in order to work out which texts within the corpus to analyse, all of the texts would first need to be analysed. Thus it was decided to base the representativeness of the sample only on title and time period. Using these two factors together, it was calculated that dividing the number of texts in each category by 3.9 and rounding the resulting number up would produce a viable sample size of 69 texts with at least one text in each group for both title and period. Sampling Methodology With the number of texts in each group decided, the next step was to select the texts themselves. For any sample like this it is vital that the sample is chosen at random rather than by the researcher, otherwise there is a risk of choosing only those texts which are easy to access, or support pre-existing theories [89]. In order to do this the entire corpus was first placed into a random order. This was achieved by adding a new column called ‘random’ to the data table containing the list of texts and then assigning a computer-generated random number to each row. Computer-generated random numbers are not truly random, they are ‘pseudo random’ in that they generally use a ‘seed’ value to run an algorithm which creates a series of numbers derived from a point within a system table [96]. However, pseudo random numbers are sufficiently random for these purposes, in that their generation is not related to the texts themselves nor pre-meditated by the survey sampler and so cannot be predicted in advance. Once these random numbers were generated they were used as the basis of a new sort order for the corpus. To illustrate how this worked, Table 3.5

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Table 3.5 First ten texts in corpus in new randomised order Text

Cover date

Random

Original order

Super-Villain Team-Up #12 The Marvel Super Heroes #12 Daredevil #38 Spidey Super Stories #19 Fantastic Four #128 Fantastic Four #247 The Uncanny X-Men #147 Fantastic Four #85 Marvel Graphic Novel #27 Fantastic Four #19

Jun-77 Nov-66 Mar-68 Oct-76 Nov-72 Oct-82 Jul-81 Apr-69 Jan-87 Oct-63

7 470 471 562 678 866 1347 1369 1443 1604

113 245 32 107 73 177 155 38 231 11

shows the first ten texts in the corpus when placed in the new randomised order, with their original ordering shown in the final column. With the corpus ordered in this way it would be possible to take the first 69 texts and be assured that it was a completely random sample, in that they were selected without reference to any particular bias or judgement. However, though random such a sample would be unlikely to be representative of the corpus as a whole. It might be, for instance, that every issue of Fantastic Four appeared within those first 69 texts, while none of the cartoon series did, or that one period was more heavily represented in the first 69 texts than the others. In order to avoid these problems Stratified Random Sampling was used. This involves dividing the corpus into strata (in this case by series and period) and then selecting from the randomised list so that the eventual distribution by strata is the same in both corpus and sample, thus ensuring that the latter is representative of the former [97, 98]. For example, if you had a list of 100 household pets and wanted to create a sample of 10 which were representative of the whole by type of animal you could place them in random order and then go through selecting each one until you had enough of that type of animal. If, say, the original corpus had 70 cats you would go through the randomised list until you had 7 cats and then ignore all cats after that, and so on for dogs, gerbils, goldfish or, in Doctor Doom’s case, tigers. Doing this for one strata is fairly straightforward, but using two was more complicated as the sample had to be representative in both ways. The first stage of the process was similar, in that the corpus was placed in a randomised order and then each text was checked to see if including it in the sample would exceed the quota for either its title or period. If it would not exceed either quota then it was included, otherwise it was excluded. For example, only one issue of Daredevil was required to be included in the sample, and the first to

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appear in the randomly ordered list was Daredevil #38. Thus, when the next issue of to appear in the re-ordered list was reached—Daredevil #19—it was excluded from the sample and the process moved onto the next text, Secret Wars II #7, which did not exceed any sample sizes and so was included. In this way the sample was gradually built up, and the process worked well until 63 of the 69 texts had been selected. This next text in the list was Not Brand Echh #11, which should not have been included because doing so would exceed the required number of texts from the ‘Creation’ period. However, more issues of Not Brand Echh were needed to make the sample representative for this series, but as they were all published during this period the selection rules meant that it would be impossible to include any more, and so the sample could not be representative. This problem was solved by moving back through the sample to find the most recently added text from this period which could be deselected without causing a similar problem. The text removed was Incredible Hulk #144, which could be safely deselected because other texts in this series had been published in the other two periods. Once this text was removed the process once more moved on as before until 69 texts had been selected. The final text added was The Amazing Spider-Man newspaper strip story ‘Doctor Doom and the Flying Saucer’ from March 1982, the 257th text out of the randomised 266. The texts selected for the final sample are indicated as such in Appendix B. With the corpus and sample thus identified it was time to move on to the analysis. However, initial attempts to use the unified catalogue of transmedia character components highlighted further practical problems around identifying exactly which ones to look for. The final section in this chapter will demonstrate a way around these problems.

The Signifier Survey The main problem with entering data in the thirteen dimensions identified was working out what to look for. Reading, viewing or listening to the texts themselves was not difficult, but then trying to note all of the characteristics within each component was often very difficult indeed, and fraught with dangers of bias. For example, as a life-long fan of Marvel comics, there are certain words and phrases (many of which would be considered offensive today) such as ‘dolt’ or ‘cretin’1 that I associated with Doctor Doom and so these would be the signifiers—the textual items that say ‘this is Doctor Doom’—that I would automatically look for. However, other such signifiers might also be present in the text—for example, his habit of speaking in the third person or saying ‘Bah!’—which I would either not be aware of or, especially in the case of speaking in the third person, be so used to that they would not register as worth noting. 1 Doctor Doom’s frequent use of words such as this would be considered offensive today, and indeed the modern version of the character no longer uses them.

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This was the case for all thirteen dimensions of the catalogue, although slightly less of a problem for components within the behavioural and authorship categories, as here there were methodologies to follow, like the BFI, or a straightforward way of noting down authors. However, for character and storyworld components any analysis would rely heavily on pre-existing ideas of what the characters’ signifiers were likely to be, and so risk simply confirming these ideas rather than discovering new ones. This is a criticism that could also be aimed at many of the existing models of transmedia character components. Uricchio and Pearson’s model of transmedia character components, for example, was developed to codify what they already knew about Batman for a book specifically about that character, rather than as method for collecting new information about any character [99]. Relying on pre-existing personal knowledge would also make the methodology described here less useful for any future researchers investigating other characters. My knowledge of Doctor Doom is based on decades of comics reading and an intense four-year period of close reading and blog writing, and it would be impractical to expect any other researcher to have similar experience before even beginning to analyse a character. The solution to the problem of relying on one opinion was simple in theory, if work-intensive in practice: to ask for other opinions via crowdsourcing. ‘Crowdsourcing’ is a term originally coined by Jeff Howe which came to be defined as ‘The act of taking a job once performed by a designated agent (an employee, freelancer or a separate firm) and outsourcing it to an undefined, generally large group of people through the form of an open call, which usually takes place over the Internet’ [100, 101]. It is a process which has been used for a huge variety of purposes where a person or organisation wants to gather ideas from outside their own resources, including bands asking for suggestions of where to tour, the Obama administration asking for questions for town hall debates, and, notoriously, the British Antarctic Survey poll to name a boat which was eventually called ‘Sir David Attenborough’ despite ‘Boaty McBoatFace’ winning the public poll [102]. For the Doctor Doom analysis crowdsourcing was used to generate a starting point for quantitative data gathering by asking a large group of people for their suggestions of what the character’s signifiers were in each of the thirteen dimensions. It is important to re-state that this was purely to find a starting point, and not an attempt to find a definitive answer to what Doom’s actual signifiers are—as will be shown in the results, respondents did not all have a comprehensive knowledge of Doctor Doom and so many answers referred to aspects of the character that they had mis-remembered, or sometimes even made up. This led to some interesting differences between the results of the signifier survey and the analysis of the sample, with some signifiers that survey respondents strongly identified appearing rarely or not at all, and also many signifiers appearing in the sample analysis that were not mentioned at all. This will be looked at in-depth in the final ‘Discussion’ chapter.

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Survey Design The initial design of the survey was based on personal experience of survey design over many years, notably on The National Evaluation Of Sure Start mentioned earlier [90]. This earlier evaluation involved the design of questionnaires conducted by researchers within the homes of participants, which required computer systems that were robust, straightforward and easy to administer. Advice was also taken from a communications professional and member of the Chartered Institute of Public Relations, who was heavily involved in the overall survey design and ensuring that the finished form was comprehensible and reliable [103]. The survey used for this project was self-administered, and so clear guidance was required in order for respondents to know what was expected of them so they could answer effectively. This included the survey’s purpose, who was conducting it, and how long it would take to complete. It was split into six sections: ‘Doctor Doom’ (Character-Specific Components), ‘Doctor Doom’s World’ (Storyworld-Specific Components), ‘Creators And Marketing’ (Authorship Components), ‘Anything Else’, ‘Your Experience Of Doctor Doom’ and ‘Mailing List’. The first three sections related to three of the four component groupings in the unified catalogue of transmedia character components—the survey was undertaken during an early stage of the research process, before ‘Behaviours’ had been moved out of the character-specific components and into a grouping of its own, and so was not included here. To aid understanding, each question came with examples of possible answers. For instance, when asking about other characters associated with Doctor Doom the example given was ‘for Sherlock Holmes you might enter “Doctor Watson, Mrs Hudson, Professor Moriarty, Inspector Lestrade” or for Harry Potter “Hermione Granger, Ron Weasley, Dumbledore, Snape, Voldemort”’. Throughout the questionnaire a range of different characters from different media were used as examples to ensure that respondents were not led to compare Doom to any particular character, nor to think their answers should be based in any particular media. Following this the ‘Anything Else’ section gave respondents the opportunity to record anything else that they did not feel had been covered so far, then ‘Your Experience Of Doctor Doom’ gathered some information about the respondents’ previous experience of Doctor Doom in comics, movies, animated TV shows, video games, newspaper strips, radio shows, action figures, toys or other media. The answers to these questions were then used to decide on additional questions so that, for example, if they had experienced Doctor Doom in animated TV shows then they were given a list of known animated TV series and asked which they had watched. Most questions gave an option to answer ‘Yes’, ‘No’ or ‘Don’t know’. The main exception in this section was the questions about respondents’ familiarity with different eras of comics, which used a Likert scale—a five-point scale used

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to measure variations in response, such as levels of agreement, importance or frequency [104, 105]. This was used to give more relevant information about the depth of respondents’ familiarity with each era, rather than using a ‘yes’ or ‘no’ question which would simply have confirmed whether or not they were aware that comics existed in this period. Decades of comics were listed, starting in 1960 and leading to the present day, with respondents asked to give their familiarity with each period on a scale of one to five, where one was ‘Not familiar at all’ and five was ‘Very familiar’. The final section appeared after the other data had been submitted, where respondents were given the opportunity to add their email address to a mailing list for future updates. This was the only personal data recorded as part of the survey and was stored in a separate table, with no link to the rest of the data, ensuring that the survey responses were completely anonymous [106]. Consent was asked to store this data, and a detailed guide on how to remove consent was given at this point, as well as in a follow-up email. Setting Up the Online Survey The survey was to be carried out online, and so some sort of survey system was required to run it. The initial intention was to use a commercial online survey tool such as Survey Monkey [107]. However, the free version was limited to just ten questions and would not allow the separation of email addresses from other responses, while the paid-for version was beyond the limited resources of this project. Other commercial packages were similarly limited, so a bespoke system was built using the PHP scripting language and a SQL server database [68, 108]. Cascading Style Sheets, a way of applying greater design flexibility to webpages, were used to make the interface more attractive for users [109]. Developing this system took much more time and effort than using an off-theshelf product like SurveyMonkey, but once completed it offered an adaptable, easily comprehensible system that was unfettered by limits on questions or number of respondents (Fig. 3.3). With the system set up a pilot survey was conducted. This is a common practice in research studies, whereby a small group of respondents are invited to try using the tool in order to identify any remaining issues, such as problems with data entry or descriptive text [110]. The testing group was recruited by contacting a small group of friends and colleagues who had experience of Doctor Doom but who were not necessarily experts in computer surveys. Four people responded to this request and took part in the pilot, with several issues being spotted, notably problems related to different internet browsers, and these were corrected before the main launch. Another useful aspect of the pilot was that the answers given by respondents showed that they understood what was being looked for, demonstrating that the questions were phrased correctly.

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Fig. 3.3 Sample page from the online survey

With that completed the next step was to find an audience for the main survey. Finding an Audience The first attempt at recruiting respondents involved sending a link and details of the survey to The Montgolfier Group (a comics reading group based around the Comics Research Hub at UAL) and the longstanding listserv community at Comics-Scholars-L, asking them to take part and, if possible, pass the information on to others [111, 112]. This brought in around 20 survey completions (exact figures about which responses came from which source are unavailable as the survey was anonymous). Although these groups were very helpful, more responses were needed, so next details were posted on social media asking for more participants. This was done via the twitter account Marvel Age Doom, which had originally been set up to alert people to new posts from my process blog [113]. Every day for

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four days the account sent out a daily tweet asking people to take part in the survey, followed by a series of individual tweets to comics-related accounts in a different category each day, covering comic shops, podcasts, comics news sites and comics professionals. For example, on 6 April 2020 the following message was tweeted from the account, asking for people to take part and retweet (‘RT’) the appeal for participation. It included a link to a blog about the survey and a specially prepared image of Doctor Doom. Have you got 20 minutes to answer some questions about Doctor Doom for my PhD? It would be very much appreciated if so - details here and please RT! http://www.mjhibbett.co.uk/doom/showblog.php?blogid=3352.

This was then forwarded to specific individuals with a personalised message. Figure 3.4 shows a tweet from 6 April 2020 which was sent to Ryan North, a member of the comics professionals group. The success of this practice varied widely according to which group was contacted. Very few comic shops or comics news sites responded at all, whereas podcasts and comics professionals reacted surprisingly enthusiastically, with a great deal of interest expressed and almost 100 survey responses received each day. Take-up slowed abruptly once this tweeting ended. A few more surveys trickled in over the next few days, but by 16 April 2020 it had come to a complete stop, with a total of 225 surveys completed. At this point the survey was closed, with the link from the website removed and a note added to the main page saying that it had finished. Cleaning the Data The data submitted was extracted from the online database system and imported into a Microsoft Access database. Microsoft Access is a desktop, rather than online, database which comes with functionality that makes it easy to manipulate simple datasets without the need for programming complicated queries, making it a very useful tool for basic data cleaning [114]. The first step in cleaning the data was to go through the textual answers in the first four sections of the survey. Here respondents were able to give as many responses as they wished, divided by commas or semi-colons. The example below shows one response to question 6 (‘Other characters’): Reed Richards, Susan Richards, Johnny Storm, Ben Grimm, Namor the SubMariner, Doom Bots

By using commas as dividers, it was straightforward to split their answer into separate responses in a new table for analysis, with one row per answer as

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Fig. 3.4 Tweet asking for help with survey

shown in Table 3.6, with ‘SurveyID’ referring to the system-generated unique identifier for that respondent’s questionnaire. Unfortunately it was not possible to do this uniformly for every respondent, as the great majority ignored the formatting requested and instead gave responses such as the one below. There is one regular butler/assistant kind who Jack Kirby drew in kind of Hungarian peasant clothing? His mother, father and members of their traveller community. Doombots.

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Table 3.6 Survey response recoded

SurveyID

Other character

588 588 588 588 588 588

Reed Richards Susan Richards Johnny Storm Ben Grimm Namor the Sub-Mariner Doom Bots

The FF. His son Kristoff. Perhaps some kind of mystical adviser (Latverian)? Agatha

This respondent used commas, carriage returns and full stops to divide their answers, and included a great deal of extra commentary which would be impossible to reduce to single answers using the original method described above. Other responses, such as the following answer to question 2 (‘Names and Titles’), were more conversational and vague: Doom, Viktor. Probably Sire or Master from his subjects in Latveria (?). I guess the Thing probably has some nickname for him like ‘Vicky’. He probably uses his full name a lot, in third person

Respondents used, and mixed together, so many different types of answer and punctuation that it quickly became clear that it would be impossible to automate the process of extracting individual data points as intended. Thus they had to be recoded one at a time by hand. This was done by first of all reading through the first 20 responses and noting down any answers which appeared more than once. Table 3.7 shows this initial table created for ‘Names And Titles’. The code ‘Other’ was added to enable the inclusion of any answers which did not fit this coding system, with a text box available to record what they were. At regular intervals, generally every 50 responses, these ‘Other’ responses would be re-evaluated to see if any new answers were appearing regularly. If this was the case they would be added to the recoded list, and previous answers of this kind would be recoded accordingly. Examples of answers added for ‘Names And Titles’ during this process included ‘Doomsie/ Doomie/Doomsy’, ‘Emperor/God Emperor Doom’, ‘King of Latveria’, and ‘Monarch of Latveria’, with slightly different versions of the same name, such as ‘Doomsie/Doomie/Doomsy’, being grouped together.

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Table 3.7 Initial coding for ‘Names And Titles’

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Signifier

Text

1 2 3 4 5 6 7 8 9 10 11 12

Doom Victor Victor von Doom von Doom Master Dr/Doctor Doom Ruler of Latveria Sire Lord Doom Vic Doc Doom Other

This was a lengthy and laborious process, and it very quickly became clear that 225 responses were much more than were actually needed. In every category, almost all of the main responses appeared within the first 50 or so surveys analysed, and no significant new responses ever arose after 100. Despite this all responses were coded, but I would heartily advise any future researchers using this method to aim for a maximum cohort size of around 50 respondents for this section of the process. Some categories were much easier to recode than others. ‘Other Characters’, for example, was fairly straightforward, as there were only a limited number of characters that Doom interacted with, and they tended to have clearly defined names. Further work was still required, however, largely due to confusion around some of Doom’s own supporting cast. Many respondents knew that he had a lost love, a faithful manservant, and an adopted son, but could not name them. As the close reading of the corpus had revealed only one lost love, faithful manservant or adopted son for Doom, however, these could be recoded as ‘Valeria’, ‘Boris’ and ‘Kristoff’ with some confidence. Several problems arose as a result of respondents going outside the intended parameters of the survey. The question ‘Previous Events’ explicitly asked respondents to list important events in the fictional life of Doctor Doom’s life, but many answers related to the respondent’s personal recollections, discussing the stories that they remember most fondly for themselves. In other cases, answers were frustratingly vague, such as ‘fighting the Fantastic Four’. The influence of internet memes was also felt, notably in the number of times the ‘Doctor Doom toots as he pleases’ meme was referenced. It was also noticeable that some respondents saw the survey as a chance to performatively show off their knowledge of the subject, despite the fact that it was clearly described as an anonymous survey with an audience of one person. This was especially notable in answers to the ‘Other’ category,

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in which some respondents went to great lengths to impress with their knowledge and opinions. Film adaptations were often mentioned here, with every single respondent who mentioned them also giving a review, going out of their way to point out how bad they thought these movies were. Over the course of recoding and analysis these responses became increasingly irritating to read through, which is another reason for recommending a smaller cohort. The greatest number of coding problems came from the answers to the ‘Behaviours’ question. This was partly due to the previously discussed difficulties in describing behaviours, but also because respondents would often narrativise their answers. Some examples of this are shown below. Obsesses about Reed Richards and his obvious intellectual inferiority to Doom. Obsesses about his scarred face. Thinks about his mother (and occasionally about his father). Sits on throne (not a euphemism), often with no-on else around, being lonesome. Plots (generally for world domination and/or humiliating/destroying RR & the FF). Plays chess. Makes robots. Has robots impersonate him for various reasons (sometimes with negative consequences). Doom is brilliant but arrogant. He’s one of the smartest men in the world and knows it, though he has a slight chink of insecurity when it comes to his rival, Reed Richards. He is manipulative and always plays a scheme, but he is true to the letter of his word. He identifies strongly as Latverian and gypsy,2 and will protect those he cares for at risk of his life. However, he is an authoritarian who cares nothing for freedom, believing himself to be smarter than anyone. haughty; condescending; manipulative; hates Reed Richards more than anyone; thinks/acts like he’s the smartest and strongest in the room/world; angry; refers to himself in the third person; villainous monologuing; oppresses Latveria & defends its sovereignty in the face of every world-spanning Marvel crisis event & alternate timeline; considers a few people (Valeria Richards, Layla Miller, Moon Girl) to be his equals or near-equals and treats them with slightly less condescension.

The problems with recoding data for this section were highly influential in the decision to look into other ways to collect information about behaviour, and show how useful grounded theory was in the design of this project. Signifier Survey Analysis Once the data was cleaned some basic analysis was undertaken. It would have been possible to delve into the data from this survey in much more detail, and the dataset has been made available online in the hope that other researchers might wish to do so, but as the main purpose of the survey was to generate 2 In modern comics Doom is described as ‘Romani’ rather than ‘gypsy’, but this is the term used in the corpus.

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a starting point for the main analysis of the corpus sample it was not done as part of this project. Conducting this analysis brought up further issues concerning the survey tool itself, notably the fact that it would have made more sense if the section ‘Your Experience Of Doctor Doom’ had been placed first. As already discussed, many respondents saw the survey as a chance to show off their knowledge of different versions of Doctor Doom, and if they had been given the opportunity to do so in this section much earlier then they might not have felt the need to do so to such an extent elsewhere. The final version of the survey tool, in Appendix C, thus has this section moved to the start of the survey [115]. Similarly, the results of the survey itself make more sense if the section about respondents’ experience of Doctor Doom is looked at first, and so the analysis below will also feature this revised ordering. For the sake of brevity, unless otherwise indicated the tables in this section will only show answers that were given by ten or more respondents. Your Experience of Doctor Doom There were six main questions in this section, asking respondents about their experience of Doctor Doom in different media. Although the inclusion criteria for the project as a whole only included texts published between 1961 and 1987 no such restrictions were placed on this survey. This was done to avoid respondents trying to second-guess what the survey was looking for, in the hope that giving them free rein to list all aspects of Doom that they were familiar with, rather than just those that they might think they ‘should’ include, would bring in more data. As will be seen, this led to some interesting results. ‘Media types: In which of the following media have you experienced Doctor Doom stories?’. Respondents were asked to answer ‘Yes’, ‘No’ or ‘Don’t know’ to this question to indicate which types of media types they had experienced Doctor Doom in. If the respondent answered ‘Yes’ to ‘Other’ then they were asked which other media they had seen featuring Doctor Doom. The combined results are shown in Fig. 3.5. The media type most experienced was comics, although movies and animated TV shows were not far behind. This was to be expected, as most of the respondents were recruited via comics-related groups, but the prominence of Action Figures/Toys was a surprise. As will be seen throughout this analysis, respondents regularly gave answers like this that I would not otherwise have considered, demonstrating the value of crowdsourcing these ideas rather than relying on the limited experience of a single researcher. The next group of questions asked for more details about respondents’ familiarity with the different media types, and were only asked where they had indicated some experience, e.g. questions about comics were only asked for people who had answered ‘Yes’ or ‘Not sure’ to ‘Comics’ here, and so on.

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0

50

100

150

200

250

Comics Movies Animated TV shows Video games Newspaper strips Radio shows Action Figures/Toys Costumes/Cosplay MF Doom/Hip Hop Trading Cards/Tabletop games Fan-Art/Fan-fiction Prose fiction/Text Books

Fig. 3.5 Experience of media types by users

‘Comics: How familiar are you with comics from each of the following decades?’. Respondents were asked to answer on a scale of 1 to 5 where 1 means ‘Not familiar at all’ and 5 means ‘Very familiar’. Scores 1 and 2 were recoded as ‘Not familiar’, 3 as ‘Neither’ and 4 and 5 as ‘Familiar’ to produce Fig. 3.6. This shows that awareness of Doctor Doom’s appearances was fairly evenly spread across time, with a slight bias towards the 1980s, which could be accounted for by his leading appearance in Marvel Superheroes Secret Wars during this period. This might also explain why so many people thought of Doom appearing as an action figure, as this was the commercial driving force behind the creation of the Secret Wars series [116, 117]. If ‘Yes’ or ‘Not sure’ to comics : ‘Comics series: Which comics series are you aware of Doctor Doom appearing in?’. 0

20

40

60

1960-1969 1970-1979 1980-1989 1990-1999 2000-2009 2010 - present day Familiar

Fig. 3.6 Familiarity by decade

Neither

Not familiar

80

100

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Here a list of popular series featuring Doctor Doom was provided, with an option for others to be added, with the responses shown in Table 3.8. Predictably, Fantastic Four and Marvel Superheroes Secret Wars were the most popular answers here, although it was intriguing to find What If ? appearing so high in the list. Doom does appear in this series several times during ‘The Marvel Age’, but it is not one that I personally associated with him, showing again the advantages of using crowdsourcing. The high number of respondents who were aware of Doom’s appearances in Unbeatable Squirrel Girl is likely, in part at least, to be due to so many respondents finding out about the survey via Ryan North’s retweet of the call for participation. As will be seen as the analysis continues, this would also lead to some skewing of other results. There were another 74 separate series mentioned for this question, all with ten or less responses, demonstrating how widely Doom has travelled throughout the Marvel Universe. ‘Movies: Which movies do you remember seeing that featured Doctor Doom?’. Table 3.8 Awareness of comics series Comics series

Yes

Fantastic Four Marvel Superheroes Secret Wars The Avengers What If? The Amazing Spider-Man Unbeatable Squirrel Girl Doom 2099 The X-Men Infinity Gauntlet/Infinity War Secret Wars Secret Wars II Spidey Super Stories Infamous Iron Man Dark Reign Not Brand Echh Books Of Doom Super-Villain Team-Up Doctor Doom and the Masters of Evil Other Iron Man Doctor Strange and Doctor Doom: Triumph and Torment Graphic Novel Luke Cage Hero For Hire Doctor Doom (2019)

203 164 164 156 145 144 141 129 127 127 127 101 100 94 80 74 64 50 13 12 12 10

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The 2005 Fantastic Four movie was mentioned most, but only by 154 respondents. This was a low number compared to the responses to comics series, possibly due to the bias towards comics fans in survey recruitment, or alternatively because none of the films listed were particularly successful [118]. The least known (78 responses) was the Roger Corman version which has never been officially released. Nobody answered ‘Other’ for this question. All of Doctor Doom’s motion picture appearances were listed in this question, so in theory there were no others to mention, but going by responses to some of the other questions it was still a surprise that nobody tried to argue otherwise. ‘Animated TV shows: Which animated TV shows do you remember seeing that featured Doctor Doom?’. As with other questions, a list of popular series was given with the opportunity to add others. Only two other responses were given, however—one respondent wrote ‘Robot Chicken’ and the other said ‘MASH talked about him at one point’. The remaining responses are shown in Table 3.9. This list appears to favour the older TV shows, perhaps hinting that the cohort of respondents were older than the age group that other later shows would have been aimed at. ‘Video games: Which video games do you remember seeing or playing that featured Doctor Doom?’. Here around two-thirds of respondents had no knowledge of any video games featuring Doctor Doom. This might also be due to the age of the cohort, as the first such games did not appear until the late 1980s and were Table 3.9 Awareness of animated TV shows featuring Doctor Doom

Yes Fantastic Four (1967) Fantastic Four: The Animated Series (1994) Spider-Man And His Amazing Friends (1981) Spider-Man: The Animated Series (1994) The New Fantastic Four (1978) Fantastic Four: World’s Greatest Heroes (2006) Spider-Man (1981) The Marvel Super Heroes (1966) The Avengers: Earth’s Mightiest Heroes (2010) The Super Hero Squad Show (2009) The Incredible Hulk (1996) Avengers Assemble (2013) Iron Man: Armored Adventures (2009) Ultimate Spider-Man (2012) Hulk and the Agents of S.M.A.S.H. (2013) Other

85 83 79 64 60 52 50 49 48 39 34 25 22 21 11 2

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Table 3.10 Awareness of video games featuring Doctor Doom Yes Marvel vs Capcom (1995–2011) Marvel: Ultimate Alliance (2006–2019) Lego Marvel Super Heroes (2013) Marvel Heroes (2015–2016) The Amazing Spider-Man and Captain America in Dr. Doom’s Revenge! (1989) Spider-Man: The Video Game (1991) Marvel Super Hero Squad (2009–2011) Other

77 74 45 26 23 15 14 13

aimed at young audiences. The responses from those who did have knowledge of video games are shown in Table 3.10. Overall, each respondent was aware of Doom appearing in 3.89 (median answer 4) out of the 8 different media types, with the overwhelming majority (77.78%) being aware of him in 3–6 media types. Similarly, on average respondents had some familiarity (score 3–5) with 3.72 out of the 6 eras, with a slight bias, as described previously, towards ‘The Marvel Age’. There were other biases, such as for comics as a media type or for characters and creators related to the Unbeatable Squirrel Girl series, which arose as a result of the method of survey recruitment. If such a survey was to be run again much more effort would be taken to engage with fans of other media. Despite these biases the results do show that the survey respondents had a broad knowledge of Doctor Doom in different media and different time periods, and so were able to give an informed view of his character components. This was demonstrated by the responses to the other sections of the survey. About Doctor Doom This, and the other remaining sections, allowed respondents to enter as many answers as they liked in free text. ‘Appearance: Please enter as many aspects of Doctor Doom’s appearance as you can think of’. Respondents had a clear idea of what was meant by ‘appearance’ and were able to answer accordingly, as shown in Table 3.11. The value of crowdsourcing Doom’s signifiers was demonstrated many times with this question, as several aspects of Doom’s appearance were noted which I would not have thought to include myself, notably ‘Gauntlets’, ‘Eyes visible’ (often stated as ‘angry eyes visible’) and ‘Rivets’. By contrast, some aspects that I thought of as very important to Doom’s appearance were very rarely mentioned, such as the discs on his knee and elbow joints which were only mentioned five times.

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Table 3.11 Aspects of Doctor Doom’s appearance

Aspect of appearance

Mentions

Green Cape Metal armour Metal Mask Green Hood Scarred face Green tunic/skirt Gauntlets Eyes visible Gold disk clasps Leather belt Rectangular/Grill in mouth Gun on hip Square eye holes Rivets Metal Boots/Jet boots Tall Red/brown hair Other

212 187 186 119 85 57 51 47 47 32 24 23 23 19 14 12 10 74

Other characteristics were perplexing, such as the ‘D-shaped belt buckle’ mentioned by five respondents. I had never seen a ‘D’ on Doom’s belt in any media, within or without my sampling area, but when I mentioned this to a younger comics reader (my nephew Noah) he immediately knew what I meant and fetched an image of Doom from a character guide which featured a belt buckle stylised to look something like a ‘D’. ‘Names and titles: Please enter ways that Doctor Doom is addressed, either by other characters, the narrator, or himself’. This category was also straightforward to code, as respondents largely understood what was being asked for and answered accordingly, as shown in Table 3.12. A lot of these answers are variations on or parts of ‘Doctor Victor Von Doom’. These were still coded separately as, for example, addressing the character as ‘von Doom’ had very different connotations to the more familiar ‘Victor’. Similarly, the different titles relating to Latveria (‘Ruler’, ‘King’, ‘Lord’, etc.) were retained separately. ‘Doomsie/Doomie/Doomsy’ however was compacted into one simply because they were different spellings of the way that he is often referred to by the character The Thing. Almost half (46) of the answers in the ‘Other’ category were unique, and included variations on ‘Ruler Of Latveria’ (e.g. ‘Regent’, ‘Dictator’, ‘President’), descriptions made up by respondents (e.g. ‘armoured mastermind’, ‘the Fantastic Four’s greatest foe’, ‘Dubious Friend of Squirrel Girl’) or attempts to demonstrate their own knowledge by giving obscure aliases (e.g. ‘Dr. Fatalis (french name)’, ‘Rabum Alal’).

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Table 3.12 Names and titles for Doctor Doom

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87

Name or Title

Mentions

Doom Victor von Doom Dr/Doctor Doom Victor Ruler of Latveria Master Emperor/God Emperor Doom von Doom Lord Doom King of Latveria Doc Doom Vic Your highness Lord of Latveria Doomsie/Doomie/Doomsy Other

145 132 123 85 35 31 27 23 19 17 16 13 13 10 10 117

What is abundantly clear from this category is that while other titles do exist, Doom is generally called either simply ‘Doom’, various permutations of ‘Doctor Victor Von Doom’ or some version of ‘Ruler of Latveria’. ‘Physical actions: Please enter specific physical actions that you associate with Doctor Doom’. This category was much more difficult to code due to confusion about what was required, with many of the responses not being physical activities at all. Answers such as ‘Magic’ or ‘Dark Arts’, ‘Combines science and sorcery’, ‘Builds/invents things’ and ‘Time Travel’ referred to more general activities which would have been more appropriately recorded in the ‘Behaviours’ category, while a lot of the unique answers in the ‘other’ category either referred to ‘Previous Events’ (‘Putting on his mask and scarring his face forever’, ‘being attacked by squirrels’) or seemed to be completed just for the sake of putting something in (‘Doom never runs…ever!’, ‘takes more power than any mortal being should attempt to wield’, ‘finds knitting tedious’). The instructions did say, repeatedly, that answers could be left blank, but some respondents appeared to view it not as a survey but a test of their own knowledge and were determined to complete all the fields. This led to a large number of different answers, with 88 separate responses given, 42 of which were unique. The top answers are shown in Table 3.13. Despite the confusion many respondents did understand what was required, and once more the benefits of crowdsourcing in this way were made clear, with several unexpected answers proving popular. These included ‘Bolts of energy from hands’ or ‘Shakes or clenches fist/hand gestures’. Conversely, ‘looking

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Table 3.13 Physical actions

Physical action

Mentions

Bolts of energy from hands Shakes or clenches fist/hand gestures Magic/Dark Arts Dramatic pose/Power pose Flies/hovers (often with jetpack) Builds/invents things Sits on Throne Manipulates machinery (buttons/levers) Doombot (uses or is one) Monologues Curses Reed Richards Makes a speech/Addresses crowd Strides (through crowds) Arms in the air (while speaking) Fighting/hand to hand combat Rules Latveria Rants/Shouts Cape flourish Broods (often on parapets) Gives Orders Arms crossed Other

80 72 58 57 45 40 36 35 26 19 17 16 15 14 14 12 12 10 10 10 10 177

at monitors/video screen’, which to my mind is one of Doom’s defining characteristics, was only mentioned by two people. ‘Behaviours: Please enter general behaviours or personality traits that you associate with Doctor Doom’. This category was even more problematic than the previous one due to the issues already discussed around ‘Behaviour’ in the chapter about generating the full catalogue. Coding the answers here was extremely difficult due to the huge variety of responses, and in the end 233 different answers were recorded, with 150 of those being given by only one person each. Behaviours mentioned 10 or more times are shown in Table 3.14. Despite the huge range of answers given, it is clear that Doom was generally thought to be arrogant and obsessed by three things—power, Reed Richards, and the death of his mother. ‘Dialogue: Please enter things that Doctor Doom regularly says - his catchphrases’. By contrast, this question was very straightforward, with far fewer types of response. This was partly because many people found it difficult to think what Doom’s dialogue might consist of—the phrase ‘Can’t think of any specific (catch)phrases’ or similar came up so often that it was included as a category of

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Table 3.14 Behaviours

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89

Behaviour

Mentions

Arrogant Megalomania Genius/High Intelligence Reed Richards obsession Egotistical/Narcissistic Scheming/Plotting/Devious Concern for his own country and people Vengeful Authoritarian/Tyrannical Mother Obsession Angry Sense of justice/honour/chivalry Condescending/haughty regal/imperious/aristocratic Vain proud/prideful Jealous Speaks in third person Commanding driven/self-motivated Evil Pompous/verbose Cruel self-aggrandising soft spot for Valeria Richards/other children Confident Boastful

88 77 63 60 54 43 33 26 25 25 25 24 22 22 20 20 17 15 14 13 13 12 11 11 10 10 10

its own. Also, as can be seen in Table 3.15, there were several popular answers which referred to a general way of speaking rather than specific phrases. The presence of ‘Confound these squirrels!’ shows yet again the influence of Ryan North’s followers, as Doom said a version of this phrase (‘Confound these wretched rodents!’) in the first ever appearance of Squirrel Girl Marvel Superheroes #8 [119] and was referred to several times in North’s stories.

Doctor Doom’s World The answers in this section were generally easier to code than those related to Doom himself as respondents were asked to give answers within a much more specific range. However, there was still some confusion about what categories some answers should go into. ‘Other characters: Please enter any other characters who regularly appear in Doctor Doom’s stories - his supporting cast’.

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Table 3.15 Things that Doctor Doom says

Dialogue

Mentions

Speaks in Third Person Doom used in various self-aggrandising statements Richards! Fool(s) Can’t think of any specific (catch)phrases I am Doom Accursed (Reed) Richards Kneel/Bow before Doom Confound these squirrels! Bah! Doctor Doom does/toots as he pleases You dare/how dare you? Says his own name a lot Other

49 42 36 34 32 31 21 18 14 11 9 9 6 75

This question provides a good example of the issues encountered in the section overall. While respondents found naming characters a lot more straightforward than for behaviours or physical actions, there were still some issues with the coding itself. There were 80 different characters, or sometimes groups of characters, mentioned by respondents, with 43 mentioned more than once. Table 3.16 shows those mentioned by 10 respondents or more. The group description ‘Fantastic Four’ generated issues with coding, as some respondents would enter the name of the group as a whole while others would list the individual names of the four characters. In both cases this was coded as a single answer, ‘The Fantastic Four’. However, a large number of respondents gave both ‘Reed Richards’ and ‘The Fantastic Four’ as separate answers, seeming to indicate that they thought Doom and Reed Richards had their own relationship apart from the group, and here both answers were retained. There were also many occasions where respondents would only name Reed Richards, and not the team at all. The only other member of the Fantastic Four to appear apart from the group was Sue Storm/Richards, although much less often. Another coding issue was that respondents had difficulty in remembering the name of Doom’s manservant Boris’s name, although they usually described the character well enough to be clear who they meant. A similar problem occurred around the fact that there are two characters in Doom’s storyworld called Valeria—his childhood sweetheart, and Reed and Sue’s daughter, who is named after her. Usually respondents would make clear which one they meant by prefacing it with terms such as ‘his lost love…’ if they meant Doom’s childhood sweetheart, or giving the full name ‘Valeria Richards’ if they meant

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Table 3.16 Other characters

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Character/group of characters

Mentions

Fantastic Four Doombots Sub-Mariner Kristoff/son Boris Doctor Strange Iron Man Valeria Richards Mother (Cynthia) Reed Richards Spider-Man Mephisto Latverian citizens Silver surfer Franklin Richards Avengers Squirrel Girl Valeria (true love) Galactus Other

191 58 53 49 37 35 32 28 27 27 26 25 23 21 20 20 19 16 10 136

his goddaughter.3 However, on other occasions this had to be worked out through context, so that if the respondent included ‘Valeria’ directly after the names of individual members of the Fantastic Four it was assumed to mean Valeria Richards, but if it appeared alongside Boris or Doom’s mother then it would be recoded as his childhood friend. Valeria Richards is one of only two characters in this list who first appeared in Doom’s storyworld after 1987. Indeed, most aspects were defined within the character’s first few years of existence and were little changed afterwards, with one exception being Kristoff, who was introduced by John Byrne during the Consolidation period of ‘The Marvel Age’ but, according to The Marvel Database, went on to make over 70 further appearances in the Marvel Universe [120]. ‘Objects: Please enter any objects that regularly appear in Doctor Doom’s stories’. Respondents mentioned 79 different objects in this category, although only a third (26) were mentioned by more than one person. Table 3.17 shows those mentioned at least ten times.

3

Reed and Sue Richards’ daughter Valeria is Doom’s goddaughter, and is named after his childhood sweetheart thanks for Doom saving the life of both mother and daughter during childbirth.

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Table 3.17 Objects

Object

Mentions

Doombots/Robots Armour Time machine Machinery/Kirbytech Castle Mask Throne Cloak and/or hood Goblets Weaponry (high tech) Mad Science Device (new to this story) Magical artefacts Other

107 62 57 41 37 35 29 25 17 16 13 11 125

This category had the most overlap with others. The top item ‘Doombots’ was also mentioned in answer to ‘Appearance’ (5 responses), ‘Physical actions’ (26), and ‘Other characters’ (58). Answers such as ‘armour’, ‘mask’, ‘cloak and/or hood’ were also given for ‘Appearance’, while ‘Castle’ also appeared in ‘Locations’. There are arguments for placing these components in any of these dimensions, but for the purposes of this project only one could be used, and deciding which could not be based simply on personal preference as this would be neither reproducible nor empirical. Instead the decision was left to crowdsourcing, so that whenever this issue arose the signifier was placed into whichever component the respondents were most likely to put it into. Hence, in this case, Doombots would be regarded as ‘Objects’. After Doombots the next top answer was Doom’s time machine and then ‘Machinery/Kirby Tech’. ‘Kirbytech’ is a term widely used in comics fandom and some academic writing on Jack Kirby’s art [121, 122]. It refers to a kind of technology that was ‘never drawn to look functional; the moebius striplike masses of mazed metalwork that were a mainstay of his oeuvre were simply stylized design work, as much a recognizable architectural motif as Alexander Calder’s mobiles or Louise Nevelson’s abstract, sculptural boxes’ [123, 124]. It often appears in conjunction with ‘Kirby Krackle’, the pattern of black dots distinct to Kirby’s work, used to indicate ‘forces beyond [the characters’] control about to defeat and engulf them in incredible energy’ [125]. The terms ‘Weaponry (high tech)’ and ‘Mad Science Device (new to this story)’ might well be illustrated as Kirbytech in this way, but they were included as separate signifiers because they referred to specific aspects of Doom’s stories, with the search for new weapons or new devices often being used as a motivation for his actions.

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It was also interesting to note that only two people mentioned ‘viewing screens’. My close reading of the corpus for the Marvel Age Doom blog showed that these appeared again and again during ‘The Marvel Age’ across all media versions, and yet they do not seem to have left an impression on respondents. One explanation for this could be that the respondents were answering questions in the twenty-first century, where viewing screens are so common as not to be notable, whereas the stories themselves were written and consumed in a time when they were still the stuff of science fiction. ‘Locations: Please enter any places that you associate with Doctor Doom stories’. This was another category that was straightforward to code, with 62 distinct locations mentioned, including one clear top answer, and more than half (33) which were only mentioned by one respondent. Table 3.18 shows the most popular answers. The only problem encountered here was with the fact that Doom owns at least two castles. One is located in the Adirondack Mountains and was first seen in his debut appearance in Fantastic Four #5 [1], while the other, more often used one, in Latveria debuted two years later in Fantastic Four Annual #2 [126]. Occasionally respondents would state which one they were referring to, with ‘Castle Doom’ being assumed to mean the one in Latveria, but if the answer was simply ‘Castle’ then this was coded separately. This question showed another clear difference between my own perceptions of Doom and that of the respondents. Only four mentioned the United Nations building as a location, despite the fact that it is used again and again throughout the corpus. For example, the Spider-Man cartoon series from 1981 features it in almost every episode where Doom appears. Unlike the viewing screens in the previous category, this cannot be explained by advances in technology, so perhaps the reason is a change in the political landscape since the time of the corpus. The United Nations almost never appears in modern Table 3.18 Locations

Location

Mentions

Latveria New York Baxter building Castle Doom Castle Hell Doomstadt Latverian embassy Space Battleworld Laboratory Other

210 88 57 43 37 26 20 19 18 10 10 538

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Doom stories, and this may be because it is seen as less important as a force in global politics. ‘Previous events: Please enter any important events from the past that are referred to in Doctor Doom’s stories’. Respondents mentioned 112 different past events, with just over a third (35.7%) being mentioned by more than one person. The main responses are shown in Table 3.19. This was another instance where there was some confusion among respondents about what was required. The question asked for ‘important events from the past that are referred to in Doctor Doom’s stories’, but most people entered all the events that they themselves remembered, often acting as if it was some sort of test. This was especially noticeable in the ‘Other’ category, where long lists of stories were entered, often in vague terms, some of which were internet memes rather than actual events. The most popular responses focused on Doom’s origin story and his mother’s death and damnation. These are clearly core to Doom’s character, and the fact that very few subsequent events are referred to points again to the way Doom is often used as a ‘portable’ character who can be dropped into any storyline without need for much introduction or knowledge of any other history.

Table 3.19 Previous responses

Event

Mentions

Scarring of face (in explosion) University Early meetings with Reed Richards Death of his mother Overthrowing ruler of Latveria Mother’s damnation Mask place on face Time in Tibet/Himalayas Attempts to save mother from hell/Mephisto Gypsy life/Childhood in Latveria Armour being built Finds mother’s spells/learns magic Ongoing rivalry with Reed Richards Father’s death Steals Silver Surfer’s power/board Steals Beyonder’s Power Various confrontations with FF Other

150 83 81 78 51 49 33 32 30 30 28 23 22 20 16 16 13 141

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Creators and Marketing This section dealt with the aspects related to Doom’s existence as a fictional character in the ‘real world’, created and sold by people and commercial entities. The two main questions here provided respondents with little difficulty, and did not require a great deal of recoding. ‘Creators: Please enter the names of any people or organisations that you associate with the creation of Doctor Doom’s stories. Please note that this can refer to anybody who worked on any story, not just the original creators of the character’. The question clearly stated that it was asking about ‘anybody who worked on any story, not just the original creators of the character’, but Table 3.20 shows that Doom’s original creators, Stan Lee and Jack Kirby, were most commonly associated with the character. After Lee and Kirby are the names of writers and artists responsible for fanfavourite runs on Fantastic Four—John Byrne, Jonathan Hickman, Mark Waid and Mike Weiringo, and Walt Simonson [127–129]. Interestingly, ‘Marvel Comics’ is included in this category. This would be a better fit for the next section ‘Market Authors’, but it is interesting to see how many people think of the brand as an actual creator of Doom’s stories. As seen for other categories, the bias inherent in the selection of respondents is clear, not just the usual one towards the Unbeatable Squirrel Girl series (with both series creators, Ryan North and Erica Henderson, included in the responses), but in the fact that almost all of the names mentioned come from comics. The most popular non-comics creator was Josh Trank, Director Table 3.20 Creators

Creator

Mentions

Stan Lee Jack Kirby John Byrne Jonathan Hickman Marvel Comics Mark Waid John Buscema Walt Simonson Jim Shooter Mike Weiringo Ryan North Roy Thomas Mike Mignola Joe Sinnott Chris Claremont Roger Stern Steve Ditko

192 188 85 42 28 25 24 21 21 17 16 15 15 15 14 11 10

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Table 3.21 Marketing

Creator

Mentions

Marvel Comics Marvel Stan Lee 20th Century Fox Fox studios Jack Kirby Disney Other

111 84 68 28 26 25 12 113

of Fantastic 4, with 7 mentions, followed by Roger Corman, director of the unreleased 1994 Fantastic Four movie (5 mentions), Tim Story, the director of 2005 Fantastic Four and 2007 Fantastic Four: Rise Of The Silver Surfer movies (5) and Julian McMahon, the actor who played Doom in both of the latter (4). ‘Marketing: Please enter the names of any people or organisations that you associate with the marketing of Doctor Doom’s stories. This could include names that you have already entered in the previous question, but does not have to’. This question refers to market authors although, as with the previous question, a simpler term was used. There were 75 market authors named, with over two-thirds (53) only being mentioned by a single respondent. Table 3.21 shows those named by 10 or more respondents. Unsurprisingly, ‘Marvel Comics’ and ‘Marvel’ were mentioned most here, along with the different movie studios that have produced Doom’s appearances. Similarly, the creators named are broadly in line with the top answers from the previous category, as these names are often used in conjunction with collected editions. Also, most Marvel comics from the mid-1970s are headed with ‘Stan Lee Presents…’ so it would be expected that his name should be quite high in the list. The only surprise, for me at least, was the appearance of the rapper MF Doom in the table. When this research began I was completely unaware of his work, although given the amount of times people have mentioned him to me since then, perhaps I shouldn’t have been.

Anything Else ‘Other associations: Is there anything else that you associate with Doctor Doom that has not been covered in this survey so far?’ This final category was included so that respondents could mention anything else that had not been covered. Most did not use it, and those who did generally took it as an opportunity to repeat things they had already been covered. Several also mentioned types of media they had seen Doom in, which

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is another argument for re-ordering the survey—if the section which asked about which media they had seen Doom in had come earlier then they might not have felt the need to report it again. The only topics brought up by more than one person which were not covered in the main survey questions were MF Doom (mentioned by 11 people) and Doom’s status as an inspiration to Darth Vader (3). If nothing else, this at least shows that nothing was missing from the survey, making it a comprehensive examination of people’s experience of Doctor Doom.

Signifier Survey Conclusion This concluding section will first of all look at the effectiveness of survey, followed by the overall results of the data analysis. Finally, it will describe how these results were adapted for practical use in the full analysis of the study sample.

Effectiveness of the Survey Overall the survey worked well, providing valuable information about the types of signifiers to look for in the main analysis, as well as giving interesting insights into what Doctor Doom meant to this group of fans. However, there were three main problems which would need to be addressed if the process was repeated for other characters. Firstly, the way that respondents were recruited led to a cohort which was clearly biased in favour of comics, and especially Unbeatable Squirrel Girl . This was not overly problematic for this character and time period, partly because this group still had a wide experience of other media, and partly because Doctor Doom appeared mostly in comics texts during this period anyway, but if this method was used for characters who appear more often in other media then more effort would need to be made to reach out to other fandoms. Related to this, although it was gratifying to have such a large cohort of respondents it was not actually necessary, and sometimes actively annoying. A smaller group taken from wider sources would have given results that were just as useful and would have taken far less time to code. The second issue was with the design of the survey itself. As noted throughout this chapter, leaving the questions about respondents’ experience of Doom in different media to the end of the survey meant that many tried to answer them earlier, eager to show off their knowledge of the character. I believe that moving this section to the start of the survey would reassure respondents that their knowledge would be used (especially for those who see it as a ‘test’), and would also give them a clearer idea of which areas were being examined. Finally, there were some questions where respondents were not always clear about what was required of them, and these should be rephrased. A prime example of this would be the ‘Behaviour’ section.

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The revised version of the survey, which attempts to address these issues, is included as Appendix C.

Overall Results Despite these problems the survey managed to elicit a wide range of responses which painted a rich, and surprisingly coherent, picture of who Doctor Doom is. The answers can be broadly summarised as follows: • He is an arrogant, megalomaniacal, egotistical genius who is obsessed with Reed Richards, the welfare of his country, and the fate of his dead mother. This is shown by his use of dramatic actions, often involving his hands, and penchant for striking dramatic poses, as well as the way he refers to himself in the third person, making self-aggrandising claims and using phrases such as ‘Fools!’, ‘Bah!’ and ‘Curse you!’. • He is generally referred to in three ways—as Doctor Doom (or simply ‘Doom’, usually by himself), variations of his full name Victor von Doom, or with honorifics referring to his status as the ruler of Latveria. • He wears a mask and suit of armour with a green tunic over the top, a hooded cloak, attached with golden clasps, and a leather belt with a gun holster. His eyes are visible beneath the mask, and his face is scarred after an accident. • The accident which scarred his face occurred while at University with Reed Richards, and is the most important event in his life, followed by the damnation of his mother. • The characters that Doom is most often associated with are the Fantastic Four (especially Reed Richards) and his own Doombots. Other close associates include Namor the Sub-Mariner, his adopted son Kristoff, and his manservant Boris, but he interacts with a wide variety of characters across the Marvel Universe. • He can mostly be found in Latveria, especially in his castle, or in New York locations such as the Baxter Building, his castle in the Adirondacks, or the Latverian Embassy. • He was created by Stan Lee and Jack Kirby, with other notable creators being John Byrne, Jonathan Hickman, Mark Waid and Mike Weiringo, and Walt Simonson. • He is a Marvel comics character. This description would, I think, be agreed as broadly correct by anybody familiar with the character. However, not all aspects of it are true across time and different media. There are, for instance, versions of Doom in other media which do not include Reed Richards at all. Similarly, there are many aspects of his character apparent from a close reading of the corpus that are missing,

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such as his use of viewing screens and many visits to the United Nations. This will be examined further in the analysis of the sample itself.

Using the Results to Generate a List of Signifiers The main purpose of the survey was to identify a list of initial signifiers for each of the thirteen character dimensions which could then be used for the main data entry, and this was done by selecting all signifiers for each dimension that were mentioned by 5% of respondents or more. In practice this meant using any signifier mentioned at least eleven times. It is important to reiterate that this was just the first part of the process— many other signifiers would be added over the course of the analysis process, including several which were mentioned by less than 11 respondents in the survey. This initial selection was done purely to create a starting point for data entry. Once the signifiers were selected in this way they were checked for any which appeared in more than one list. As discussed earlier, this was dealt with by placing them in the dimension they appeared in most often, e.g. Doombots appeared in ‘Objects’ the most (107 mentions) and so were placed here for data entry and removed from the lists for other dimensions, and the same policy was carried out for other signifiers which appeared in more than one dimension. Personally I would have placed Doombots in ‘Other Characters’, but taking the majority verdict here gave a clear strategy for categorisation that would be reproducible by other researchers. In addition, some of the signifiers’ names were adjusted to make them clearer. For instance, the phrase ‘Doom used in various self-aggrandising statements’ was taken directly from a respondent’s answer and was used as a catch-all during survey coding to include all instances of the character referring to himself as ‘Doom’. This is slightly different to ‘Speaks in third person’, which might include self-references as ‘Victor’ or ‘Your master’, so for clarity these two were coded as separate signifiers, ‘Refers to self as “Doom”’ and ‘Speaks in third person’. The initial signifiers to be looked for were thus revised, recoded and reordered as follows: Appearance Green Cape; Green Hood; Green tunic/skirt; Metal Mask; Scarred face; Eyes visible; Rectangular/Grill in mouth; Square eye holes; Rivets; Metal armour; Metal Boots/Jet boots; Gauntlets; Gold disk clasps; Leather belt; Gun on hip; Tall.

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Names and Titles Dr/Doctor Doom; Doom; Victor; Victor von Doom; von Doom; Vic; Lord Doom; Doc Doom; Ruler of Latveria; King of Latveria; Emperor/God Emperor Doom; Master; Your highness.

This category features many variants on ‘Dr Doom’, but all are included because they are used differently within the texts. For instance, the name ‘Doc Doom’ is used mostly by The Thing when he is treating the character as an old acquaintance, while courtiers tend to use ‘Lord Doom’. Physical Actions Bolts of energy from hands; Shakes or clenches fist/hand gestures; Dramatic pose/Power pose; Fighting/hand to hand combat; Performs magic; Builds/ invents things; Manipulates machinery (buttons/levers); Flies/hovers (often with jetpack); Monologues; Rants/Shouts; Makes a speech/Addresses crowd; Arms in the air (while speaking); Sits on Throne; Strides (through crowds).

It is highly debatable whether all of these actions actually are physical actions—ranting/shouting or making speeches could be part of the ‘Dialogue’ category, for example, although using one’s voice at a higher volume or performing in front of a crowd is also a physical action—but the policy of placing signifiers in categories according to the survey results meant that this is where they belonged. Behaviour The problems in captured ‘Behaviour’ as a single component were clearly demonstrated by the difficulties gathering and recoding information for this section here, and indeed the need to create a separate ‘Behavioural’ group of components was made clear by these experiences. Thus, the results of the survey could not be used here. Dialogue Speaks in third Person; ‘I am Doom’; ‘Kneel/Bow before Doom’; Other references to self as ‘Doom’; ‘Richards!’; ‘Accursed (Reed) Richards’; ‘Fool(s)’; ‘Confound these squirrels!’; ‘Bah!’

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Other Characters Fantastic Four; Boris; Kristoff; Mother (Cynthia); Valeria (true love); Latverian citizens; Reed Richards; Valeria Richards; Franklin Richards; Sub-Mariner; Doctor Strange; Iron Man; Spider-Man; Mephisto; Silver surfer; Squirrel Girl.

Objects Doombots/Robots; Time machine; Machinery/KirbyTech; Weaponry (high tech); Mad Science Device (new to this story); Magical artefacts; Throne; Goblets.

Locations Latveria; Doomstadt (Latverian capital); New York; Latverian embassy in New York; Baxter building; Castle Doom; Castle in New York; Castles (other); Hell; Space.

Previous Events Death of his mother; Father’s death; Mother’s damnation/Attempts to save mother from Mephisto; Gypsy life/Childhood in Latveria; Finds mother’s spells/learns magic; Time at University; Early meetings with Reed Richards; Scarring of face (in explosion); Time in Tibet/Himalayas; Armour being built; Mask placed on face; Overthrowing ruler of Latveria; Ongoing rivalry with Reed Richards; Steals Silver Surfer’s power/board; Steals Beyonder’s Power; Various confrontations with FF.

Textual Authors Stan Lee; Jack Kirby; John Byrne; Jonathan Hickman; Marvel Comics; Mark Waid; John Buscema; Walt Simonson; Jim Shooter; Mike Weiringo; Ryan North; Roy Thomas; Mike Mignola; Joe Sinnott; Chris Claremont; Roger Stern.

Market Authors Marvel Comics; Marvel; Stan Lee; 20th Century Fox; Fox studios; Jack Kirby; Disney.

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With these initial signifiers identified, the next stage was to start collecting data for analysis.

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13, 2018. https://lostmediawiki.com/The_Marvel_Super_Heroes_(lost_live-act ion_segments_from_WNAC-TV_in_Boston;_1966). Thomas, Roy, John Buscema, and Joe Sinnott. 1974. “The Way It Began.” Power Records (Marvel Comics) 1 (13). 101. Kirby, Adrienne, Val Gebski, and Anthony C Keech. 2002. “Determining the Sample Size in a Clinical Trial.” Medical Journal Australia 177: 256–257. Myers, Jerome L, Arnold D Well, and Robert F Lorch Jr. 2010. “Developing Fundamentals of Hypothesis Testing Using the Binomial Distribution.” In Research Design and Statistical Analysis, 65–90. New York: Routledge. 103. Walker, Ian. 2010. Research Methods and Statistics. Basingstoke: Palgrave Macmillan. Melhuish, Edward, Jay Belsky, and Jacqueline Barnes. 2010. “Sure Start and Its Evaluation in England.” In Encyclopaedia on Early Childhood Development, by RE Tremblay, RG Barr, RDeV Peters and M Boivin, 1–7. Montreal, Quebec: Centre of Excellence for Early Childhood Development. Qualtrics. 2019. Sample Size Calculator. Accessed April 20, 2020. https://www. qualtrics.com/blog/calculating-sample-size/. 106. Mautner, Gerlinde. 2016. “Checks and Balances: How Corpus Linguistics Can Contribute to CDA.” In Methods of Critical Discourse Studies, edited by Ruth Wodak and Michael Meyer, 154–179. London: Sage. Corpas Pastor, Gloria, and Miriam Seghiri. 2010. “Size Matters: A Quantitative Approach to Corpus Representativeness.” In Language, Translation, Reception. To Honor Julio César Santoyo, edited by R Rabadán, 111–146. León: Universidad. Biber, Douglas. 1993. “Representativeness in Corpus Design.” Literary and Linguistic Computing 8 (4): 243–257, p. 243. Gray, Bethany, Jesse Egbert, and Doug Biber. 2017. Exploring Methods for Evaluating Corpus Representativeness. Birmingham: Corpus Linguistics International, p. 2. Haahr, Mads. 2020. Introduction to Randomness and Random Numbers. Accessed May 19, 2010. https://www.random.org/randomness/. The Pennsylvania State University. 2018. Sampling Theory and Methods: How to Use Stratified Sampling. Accessed 3 20, 2020. https://webcache.googleuse rcontent.com/search?q=cache:BEnyh0bfUIQJ:https://online.stat.psu.edu/sta t506/node/27/+&cd=1&hl=en&ct=clnk&gl=uk. Salkind, Neil J. 2010. Encyclopedia of Research Design. Sage Research Methods. Pearson, Roberta, and William Uricchio. 1991. The Many Lives of the Batman: Critical Approaches to a Superhero and His Media. London: Routledge. Howe, Jeff. 2006. “Wired: The Rise of Crowdsourcing.” 1 6. Accessed 2 19, 2020. https://www.wired.com/2006/06/crowds/. Safire, William. 2009. “On Language: Fat Tail.” New York Times, 5 2. Accessed 2 19, 2020. https://www.nytimes.com/2009/02/08/magazine/08wwln-saf ire-t.html?_r=3&ref=magazine&. Livescault, Jonathan. 2019. What Is Crowdsourcing? (in 2019). Accessed 2 19, 2020. https://www.braineet.com/blog/crowdsourcing/. Wadsworth, Charlotte. 2020. Consultations on Survey Design (18–28 February). 121. Likert, R. 1932. “A Technique for the Measurement of Attitudes.” Archives of Psychology 130: 1–55.

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105. McLeod, SA. 2019. “Simply Psychology: Likert Scale.” 3 August. Accessed Jun 4, 2020. https://www.simplypsychology.org/likert-scale.html. 106. Information Commissioner’s Office. 2020. Guide to the General Data Protection Regulation (GDPR). Accessed Jun 6, 2020. https://ico.org.uk/for-organisat ions/guide-to-data-protection/guide-to-the-general-data-protection-regulationgdpr/. 107. Survey Monkey. 2020. Survey Monkey: About Us. Accessed Jun 5, 2020. https:// www.surveymonkey.com/mp/aboutus/?ut_source=footer. 108. PHP.net. 2001–2018. History of PHP. Accessed May 13, 2018. http://php. net/manual/en/history.php.php. 109. W3C. 2020. “Cascading Style Sheets.” 9 July. Accessed July 13, 2020. https:// www.w3.org/Style/CSS/Overview.en.html. 110. 128.Lancaster, GA, S Dodd, and PR Williamson. 2004. “Design and Analysis of Pilot Studies: Recommendations for Good Practice.” Journal of Evaluation in Clinical Practice 10 (2): 307–312. 111. CoRH!! 2019. Comics Research at the University of the Arts London. Accessed Jun 8, 2020. http://comicsresearch.arts.ac.uk/. 112. COMIXSCHOLARS-L. 2012. COMIXSCHOLARS-L Home Page. Accessed Jun 8, 2020. https://lists.ufl.edu/cgi-bin/wa?A0=COMIXSCHOLARS-L. 113. Hibbett, Mark. 2017. Marvel Age Doom (Twitter Account). Accessed Jun 8, 2020. https://twitter.com/marvelagedoom. 114. Microsoft. 2020. “Database Software and Applications.” 13 July. Accessed July 13, 2020. https://www.microsoft.com/en-gb/microsoft-365/access. 115. Hibbett, Mark. 2020. “Doctor Doom Survey: Results and Survey Forms.” University of the Arts, London. 21 July. https://doi.org/10.25441/arts.126 83753.v1. 116. Shooter, Jim. 2011. Secrets of the Secret Wars. 4 April. Accessed January 2018. http://jimshooter.com/2011/04/secrets-of-secret-wars.html/. 117. 135.Howe, Sean. 2012. Marvel Comics: The Untold Story. New York: Harper. 118. Clark, Travis. 2019. “After Several ‘Fantastic Four’ Flops, Disney Should Use Its Upcoming Netflix Competitor to Revamp the Franchise.” Business Insider. 21 March. https://www.businessinsider.com/disney-revamp-of-fantas tic-four-in-marvel-cinematic-universe-analysis-2019-3?r=US&IR=T. 119. Murray, Will, and Steve Ditko. 1991. “The Coming Of Squirrel Girl.” Marvel Superheroes (Marvel Comics) 1 (8). 120. Fandom. 2020. Kristoff Vernard (Earth-616). 23 June. https://marvel.fandom. com/wiki/Kristoff_Vernard_(Earth-616). 121. Alexander, Mark. 2012. Stan Lee & Jack Kirby: The Wonder Years. Raleigh, NC: Two Morrows. 122. Hatfield, Charles. 2015. Hand of Fire—The Comics Art of Jack Kirby. Accessed January 8, 2021. https://kirbystudies.org/2015/07/09/kirbyexhibit ion2015/. 123. Taylor, Stan. 2019. “The Kirby Effect: The Journal of the Jack Kirby Museum.” 10 February. Accessed January 8, 2021. https://kirbymuseum.org/blogs/eff ect/2019/02/10/looking-for-the-awesome-20/. 124. Schumer, Arlen. 2019. The Graphic Design of Comic Book Art. Los Angeles. 125. Gandolfo, Amadeo. 2019. “The Kirby ‘Krackle’: A Graphic Lexicon for Cosmic Superheroes.” In Abstraction and Comics (Volume II), edited by Aarnoud Rommens, 321–334. Liège, Belgium: Presses Universitaires de Liège, p. 327.

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126. Lee, Stan, Jack Kirby, and Chic Stone. 1964. “The Fantastic Origin of Doctor Doom!” Fantastic Four Annual (Marvel Comics) 1 (2). 127. Burlingame, Russ. 2018. “The Fantastic Four Runs That Prove Marvel’s First Family Deserve a Second Chance.” Comicbook.com. 26 March. https://comicb ook.com/marvel/news/fantastic-four-runs-that-prove-they-deserve-better/. 128. Franke, JL. 2018. “Top 3: The Best Runs of Fantastic Four.” The Fifth World. 8 August. http://www.thefifth.world/2018/08/top-3-best-runs-of-fan tastic-four.html. 129. Marston, George. 2020. “10 Best Fantastic Four Stories of All Time.” Games Radar. 1 April. https://www.gamesradar.com/uk/best-fantastic-four-stories/.

CHAPTER 4

Analysis

This chapter will describe how the data for this project was collected and then how that data was categorised for empirical analysis using the thirteen dimensions previously identified.

Collecting the Data Data was collected by reading, watching or listening to each text in the sample and entering information on each of the thirteen dimensions of the unified catalogue of transmedia character components into a database. The types of data collected for each dimension varied, and will be discussed later in this chapter. This process of data collection was generally fairly straightforward, if somewhat laborious, and was done slightly differently for comics and non-comics texts. For comics texts data was entered for one dimension at a time across all texts. For example, a text would be read, the aspects of Doom’s appearance would be entered, and then the next text in the sample would be moved onto. When that was complete the same process was carried out for ‘Names and Titles’, then ‘Physical Actions’ and so on. Examining the comics texts in this way, as opposed to looking for all the different dimensions in each text at the same time, made it much easier to focus on the specific signifiers, even though it did mean going through every comic multiple times. In order to make this as painless as possible an online spreadsheet was set up with a row for each text and columns for the text title, a link to the data entry screen, and then a link to an online version of the text where available. This meant that it was possible to move quickly through the sample, reading © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 M. Hibbett, Data and Doctor Doom, Palgrave Studies in Comics and Graphic Novels, https://doi.org/10.1007/978-3-031-45173-7_4

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texts and entering the relevant data on them without having to go back and forth looking for where to go next. Data was entered for non-comics texts on a text by text basis, usually by watching or listening to the text in full once and then going back to the start, pausing playback as new signifiers were noticed. This was much more practical than trying to watch or listen to items like cartoons and radio shows thirteen times all the way through, as this would have taken a great deal longer than reading, or flicking, through a comic book to track down specific items. In addition, examining the non-comics texts after the comics texts meant that I was more practiced in looking for each of the signifiers. Assessing the different texts in this way was done purely for practical reasons, and it would have been perfectly acceptable to go through the process in either of the above ways for all types of text, or indeed to mix the processes further, so long as the data entry was completed for all dimensions and texts with equal rigour. Database Design For this project the information was entered into an online database. As above, this was done purely for practical purposes, and it would have been acceptable to have entered it into a straightforward spreadsheet instead—indeed, the chapter ‘A Tale of Two Menaces’ gives another example where this was done. For Doctor Doom, however, the flexibility of an online database was deemed to make it worthwhile setting one up, partly because it meant data entry could be done from any computer, but mostly because using an online database on a regularly backed-up server ensured that data would not be lost, as would be the case if a laptop was stolen or broken. The database was set up on a MySQL server, a commonly used database system which can also be used to generate website content using the PHP programming language [1–3]. The server used was the same one that hosted the Marvel Age Doom blog, so corpus data could be easily linked to preexisting information like titles, cover images, and my own blogs about each text. This would be extremely helpful for reminding me what texts were being analysed and, later on, for data cleaning. Four relational tables were set up. The central table was called ‘doomlist’, containing information about all of the texts in the corpus. This included a unique identifier for the text (‘ISSUE ID’), the series name, issue or episode number, whether or not it was part of the sample, a URL for the related blog, the name of the related cover image, and other information about that particular text, often gleaned from the original source datasets used as part of identifying the corpus. This was linked to the table ‘doomsignifiers’ (the naming convention followed Doctor Doom’s own, of naming everything after himself) by the unique identifier ‘ISSUE ID’. This table contained all of the data entered on the texts themselves, with one row for each signifier, included whether

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Doom interacted with the item or not. Designing the data entry system in this way meant that there was no limit to the number of signifiers that could be added for each text in each dimension, and also made later analysis much more straightforward. Slightly different types of information were needed for each dimension, and so details of this were stored in the table ‘doomcategories’. This gave information such as the name of the dimension, whether information about interactions needed to be gathered, and the sort of data entry form that should be used. Finally, the table ‘doomkeywords’ contained descriptions of all of the different individual signifiers that could be entered. These last two tables were used to set up data entry forms. Three types of form were used, depending on the data that needed to be collected. Simple ‘checklist’ forms were used for dimensions which had a relatively small number of possible signifiers, all of which needed to be checked for. These forms would list all possible signifiers, originally derived from responses to the signifier survey, and the user would be asked to indicate whether or not each one was present. For instance, for ‘Appearance’ all possible aspects would be listed, so that it would be possible to say for definite whether, for example, Doctor Doom’s belt could be seen in a specific text or not. Over the course of data entry many signifiers would arise that had not been part of the original list. When this happened that signifier would be added to the ‘doomkeywords’ table, and then all previous texts would be re-checked to see if it was present. This was another laborious aspect to the data entry but was essential to ensure that all signifiers were recorded. A different sort of form was used for dimensions where there was a larger number of possible signifiers, most of which would never be present in the same text. For example, there were almost 300 possible textual authors in the database, very few of which would be present in any one text—the maximum number recorded for a single text was 12, with an average of 5.77—so going through saying ‘yes’ or ‘no’ to every one of them for every text would be extremely time consuming. Thus it made sense to allow the user to select only those that were present, using a shorter series of dropdown menus to enter the ones that were present, rather than go through a long list of all those that might be. For these ‘dropdown’ forms, an additional text field was provided for including further information. For textual authors this was called ‘credited as’, allowing the user to record the actual credits for each author, rather than trying to codify them as ‘writer’, ‘penciller’, ‘inker’ and so on, as was the usual practice for other databases. Finally, a separate form was used for logging ‘Personality traits’, using the shorter BFI-10 developed by the Berkley Personality Lab [4]. Data was entered into the ten questions that form the inventory, and then these were used to automatically calculate scores in the five dimensions of the test.

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Data Entry With the database set up each of the thirteen dimensions was entered as follows. Appearance The process for logging Doom’s appearance was to first search through the text for a full-body image and a facial close-up of Doom and note all of the signifiers present there, before returning to the start of the text and checking through to the end to see if any other signifiers could be identified. For example, in Fantastic Four #85 [5], the two panels shown in Fig. 4.1 provided almost all of the information required: One common problem was assessing Doom’s ‘tallness’ as a signifier, which could only be assessed if other characters or objects of a known size were present. In the text above this was simple as Doom was often shown looming over other characters, but in many cases it could not be assessed either way. Another problem occurred in texts where Doom appeared in cameo or a single image. For instance, Fantastic Four #19 [6] only showed a head shot of Doom in a single panel, so it was not possible to judge whether he wore armour, a belt, or other signifiers as these parts of his body were not visible. In these cases any missing signifiers were marked as ‘not present’. It could be argued that these should instead be marked as ‘Don’t Know’ because, in

Fig. 4.1 Jack Kirby and Joe Sinnott depict Doom in Fantastic Four #85

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the previous example, Doctor Doom might well be wearing his armour out of shot. However, the point of this exercise is to collect information on the signifiers that are shown. Doctor Doom might be wearing any manner of other items that we cannot see, but the very fact that we cannot see them means that they are not being used as signifiers of his appearance. Thus, if they could not be seen they were recorded as ‘not present’. A further difficulty arose around subtler aspects of design. It was simple enough to say that Doom wore a green cloak, or wore an iron mask, but not to say what shade the green was, or how many rivets were on the mask, or whether the eyeholes were slanted and so on. After trying various ways to describe these design issues I decided to turn once again to the results of the signifier survey. Neither the shade of green nor any of these other considerations had been raised by any of the survey respondents so it was fair, and practical, to assume it was not immediately relevant to this particular research and so these were ignored in favour of the more general ‘green cloak’, ‘iron mask’, ‘rivets’ and other descriptions taken from the survey. Names and Titles Collecting information for this dimension involved simply going through each text and noting down the names and titles used. The only difficulties arose when certain titles could also be taken to be descriptors of behaviour, e.g. when Doom is described as ‘Master of Menace’ this could be coded as either ‘Master’ as title and ‘Menacing’ as a behaviour, or ‘Master of Menace’ as a title overall. However, the fact that ‘Master of Menace’ appeared in both the dialogue of other characters and the narrative text boxes made it clear that it was intended to be a title, and coded as such. Physical Actions The signifier survey was particularly useful for this dimension, as the results covered almost all of the actions identified for Doom within the sample, with just seven additional signifiers being added to this original list over the course of data entry. Dialogue The survey responses were very useful again here, especially when it came to identifying common Doom catchphrases such as ‘Bah!’, ‘Silence!’ and ‘Wha..??’. The main difficulty came from categorising Doom’s speech patterns. There was a very distinctive ‘Doom voice’ which could not be captured purely through individual words. For instance, he often speaks in the third person, is particularly self-aggrandising, or adopts a very formal manner when addressing

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others. The idea of Doom taking a formal tone could become subjective— one person’s idea of formality might be very different to another’s—however, much like Doom’s ‘tallness’, his formality was made clear throughout the sample by comparing it to the dialogue of other characters. Doom’s regular boasting could also be subjective, although again its constant appearance within his dialogue made it clear that it should be included. Thus for the purposes of data gathering these signifiers were logged as ‘speaks in third person’, ‘general self-aggrandisement’ and ‘formal tone’. Locations The main difficulty with this dimension was around granularity of location. It was easy enough to say that a story took place in New York, for instance, but within that it might also take place in the Baxter Building or Avengers mansion. The question that thus arose was whether these cases should be logged as ‘New York’ alone, or the buildings, or a combination of both. The process used was to only record locations that could actually be seen. For example in Marvel Two-In-One #96 [7] most of the story took place inside a hospital, but parts of it also took place in the streets of New York outside, so both ‘Hospital’ and ‘New York’ were logged. By contrast, in West Coast Avengers #23 [8] there were two scenes which took place within the Baxter Building but the city outside was never seen so was not logged. Another difficulty arose when locations were shown, but not named. This happened particularly in multi-part stories, where a location was established in a previous issue and not referred to again by name. For example, in Uncanny X-Men #147 [9, 10] most of the story took place within Doctor Doom’s castle in upstate New York, yet it was never referred to as such within this specific text. In these cases it was clear that readers would be expected to know the location from the previous issue, and so it was entered as a signifier. Finally, some recoding was needed when it became clear that certain places were becoming recurring locations. A good example of this was Doctor Doom’s control room, which was initially seen as just another room within his castle and not coded individually. However, as data entry progressed, and the control room was seen repeatedly, it became clear that this was a specific signifier and so needed to be coded as such. As with all such additions, whenever this happened all previous texts were re-checked to see if that signifier was present and, if so, logged as such. Other Characters This dimension proved to be particularly labour intensive, as there were several texts that had a huge number of supporting characters. The text with the highest number was Fantastic Four Roast #1 [11] which had exactly 100 other characters.

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Apart from the sheer number of characters, the great challenge with this dimension was identifying them all, especially in humour comics such as the above and Not Brand Echh, where large crowds of characters would appear in the background without being named. The existing data in The Grand Comics Database, although incomplete and sometimes inaccurate, proved to be a useful guide in these cases, especially with some of the more obscure characters. Objects Again, as with physical actions and dialogues, the results of the signifier survey were very helpful in this dimension for identifying which objects should be included. The only issue was the interplay with locations, and deciding whether something was a signifier on its own, or just part of the furniture of a specific location. For instance, in Doom’s control room he almost always sat in a bucket chair and looked at one or more viewing screens. The protocol used was that if an object also appeared outside that location it would be logged separately as an object, but if not it was considered part of the location. In this example, viewing screens regularly appeared in other locations, and so were logged separately, but the bucket chair was always part of the control room so was not entered on its own. Previous Events Signifiers of ‘previous events’ were very rarely seen during the early Creation period of the sample. Generally these took the form of a quick recap of Doom’s most recent appearance, included to bring readers up to date. This remained the case for non-comics texts throughout the sample, especially in cartoon series which rarely referred in detail to previous events, presumably because viewers would not be expected to have seen previous episodes. In comics texts, however, the need to highlight relevant past events grew as stories began to span several issues and even different series, and a knowledge of past continuity became more important for a full understanding of what was going on. By the time the sample reached the middle Chaos period of ‘The Marvel Age’, with stories much more serialised, it was noticeable that almost every comics text featured at least one recap of some kind. Often these recaps were a summary of the previous issue, rather than a specific ‘Previous Event’, and so these were coded simply as ‘recap of previous issue’. Whenever a specific event was mentioned (often, as time went on, with a footnote from the editor giving a reference for the original comic that it took place in) it was logged as such, but on several occasions looser coding was required. For example the coding ‘various previous encounters’ referred to the meetings between Doom and the Fantastic Four that were often referred to in the text without specifying one particular event. The survey responses proved particularly useful again here, as some of the apparently vague suggestions

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supplied there turned out to accurately reflect the way that these past events were often referred to. The code ‘Other’ was also widely used for this dimension, as there was such a variety of past events referenced that it was impractical to keep giving them each new codes. This was a side-effect of the fact that Doom appeared in so many different series, as events that had involved the hero of the series would be referred to that had nothing to do with Doom and so would be unlikely to recur in his other stories. If they did, however, previous texts would as ever be re-checked to uncover other mentions. Perceived Behaviour Perceived behaviour was recorded by reading, watching or listening to each text and noting the words used to describe Doctor Doom, whether that be by other characters, narratorial voices, or Doom himself. There were several texts where Doom was not described in any way, usually when he was shown in flashback or in cameo but also sometimes in more sizeable roles such as in Marvel Super-Heroes Secret Wars #7 [12] and Spidey Super Stories #53 [13] where no descriptive words were used at all. Personality Traits This dimension was assessed by reading, watching or listening to each text all the way through and then answering the questions in the BFI-10 scale. As this process was different from all other dimensions it felt unsatisfactory at the time, as it seemed to involve making guesses at what Doom’s personality might be based on personal experiences in a way that felt unreproducible. It is possible that these feelings also arose because the data entry here was so simple. It only took about a minute to answer the questions for each text which, after spending so long filling in data for dimensions like ‘Other Characters’ and, later, ‘Textual Authors’, felt almost like cheating. These apprehensions carried on throughout data entry, despite the fact that this method had already been shown to work on fictional characters [14]. As will be seen in the ‘Analysis’ section, the results achieved showed that it in fact it was highly effective. Motivation Recording motivations posed little difficulty, as they were generally expressed in the text, usually by Doom or the narrator. For example, in Spidey Super Stories #19 [15] the narrator stares that Doom ‘seeks to rule the world!’ and so this was entered into the database. In almost all cases, coding of motivations was based on what was explicitly stated in the text itself, so very little recoding or interpretation required.

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Market Authors For comics, market authors were generally judged to be the companies or individuals whose names appeared on the cover. If a creator was mentioned on the inside pages as ‘presenting’ the contents, rather than actively creating them, this was also deemed to be used as a market author. In almost all cases this took the form of ‘Stan Lee Presents’, with Lee’s name being used as a selling point, rather than as a contributor. For other types of text the market authors were taken from the part of the credits sequence where the person or organisation presenting the text to the audience was declared. For example, in the 1967 Fantastic Four cartoon series the credit ‘The Fantastic Four appear in Marvel Comics Magazine’ was given, with the name ‘Marvel Comics Magazine’ (sic) being used to market the contents, rather than being credited as a textual author of the cartoon. Textual Authors For comics, data for textual authors was taken from the credits boxes in the comic itself. This would usually be on the first page, as shown in Fig. 4.2, although on occasion it would appear at the end of the issue. Credits were entered exactly as they appeared in the comic, rather than using the practice adopted by the source databases of allocating names to specific fields such as ‘writer’, ‘penciller’, ‘inker’. Following this process removed the need to decide what some of the terms meant and led to the recording of many more types of credit (267 unique credit-types in total), including the traditional credits above but also more esoteric ones such as ‘unashamedly unleashed on an unsuspecting world by’, ‘mentalist’ and ‘engineer’.

Fig. 4.2 Credits box for Fantastic Four #237 [16]

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A possible downside of this approach was that creators who were not credited on a text were not included in the analysis. For example, in Fantastic Four #39 [17] ‘Frank Ray’ was credited as ‘delicious delineation’, but no such artist actually exists. It was common practice in the 1960s for artists who did regular work for DC to use a pseudonym when working for Marvel on the side, and in this case Frank Ray was the name used by the inker Frank Giacoia to disguise his involvement [18]. Also, according to the Grand Comics Database, the images of Daredevil that appeared in this issue were inked by Wally Wood, the penciller of the Daredevil series at the time, while a collage page ‘appears to be inked by Carl Hubbell’ [19]. These additional details, while interesting, were not entered into the dataset. As with other signifiers, the data collection was concerned only with what appeared within the text itself, not that which might be known, or assumed, by cognoscenti. Including only these signifiers ensured that the process was empirical and replicable, and did not rely on exterior knowledge or experience. For other types of texts, the textual authors were taken from the equivalent credits list, so for radio shows it was taken from the names read out at the end of the programme, while for animated TV shows it came from a combination of the opening or closing credits, depending on which was used by the individual text. Data Entry Conclusion Throughout the data entry process the thirteen-dimensional unified catalogue of transmedia character components proved to be a practical tool for recording information about Doctor Doom, with all signifiers being recorded and nothing observed in any of the texts which could not be placed within the model. The success here was in large part due to the iterative nature of grounded theory, in that the final model only worked so well because of the other much less successful versions that had come before it. The signifier survey also proved to be very useful, especially as a starting point for coding Doom’s signifiers. Knowing, for example, what aspects of Doom’s appearance had been highlighted by the survey made it much easier to know what to look out for in the final texts, and gave confidence that nothing was being left out. As stated in the previous chapter, it was not necessary to have such a large number of responses, but the results provided were invaluable. Once the initial data entry was completed several checks were undertaken to ensure that no data had been missed out, with simple queries used to find texts that had no data for certain dimensions. These texts were then doublechecked, and a few cases were found where data was missing, most likely due to the operator forgetting to click ‘save’ on the data entry screen. Here the operator was given a verbal reprimand and asked to do the data entry again, remembering to do it properly this time.

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Once this was complete the full dataset was extracted from the online MySQL database into a standalone Microsoft Access database so that the analysis itself could finally begin.

Analysis of Character-Specific Components The first group of components analysed were those which referred to the character, in this case concerning Doctor Doom’s appearance, names and titles, physical actions, and dialogue. Appearance Doctor Doom was visible in 67 of the 69 texts in the corpus, with the exceptions being The Fantastic Four Radio Show [20] which was audio only, and Fantastic Four #73 [21] in which Doom was present but never actually seen. Percentages for the tables in this section are thus based on these 67 texts. There were 16 aspects of Doom’s appearance that were recorded in at least 20% of texts, and these are listed in Table 4.1. This table shows that Doom is remarkably consistent, with his mask, square eye holes and green hood being seen in every single visual appearance in the sample. The triangular nose and green cape were seen in all but one each, and these were cases where Doom was either shown too small for them to be depicted, or where something else was obscuring the reader’s view. Table 4.1 Aspects of appearance

Aspect of appearance

Metal Mask Square eye holes Green Hood Triangular nose Green Cape Metal armour Rivets Circular joints on elbows/knees Gauntlets Green tunic/skirt Rectangular/Grill in mouth Eyes visible Leather belt Gold disk clasps Tall Gun on hip

Texts N

%

67 67 67 66 66 63 63 61 61 60 60 60 56 53 44 15

100.00 100.00 100.00 98.51 98.51 94.03 94.03 91.04 91.04 89.55 89.55 89.55 83.58 79.10 65.67 22.39

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Almost all of the other main items were seen in at least 80% of texts, and for these signifiers there were always practical reasons why they were not visible. These included situations where only Doom’s head was shown so items such as his armour and tunic were not seen, or he was seen in the distance so smaller signifiers such as rivets could not be made out, or when signifiers like his mouth grill or leather belt were obscured by other objects. Doom’s tallness was missing in a third of texts simply because these were cases when no other characters were nearby, and so it could not be assessed. Most of the other signifiers recorded which are not shown in the table were temporary features for specific stories, such as his red hair or scarred face in pre-accident appearances. In fact, the only major aspects of his appearance that vary in any meaningful way are his disk clasps, gun and the colour of his tunic. In Doom’s early appearances he had a single green disk clasp, which briefly became two green clasps before settling into two gold clasps that he has worn ever since. There is some crossover between the different versions, with the gold clasps first appearing in the sample in Fantastic Four #59 [22] in February 1967, while the green ones continued to appear until December 1968 in Not Brand Echh #11 [23] In other media, the green clasps appeared in the Fantastic Four cartoon series [24] and the Marvel Superheroes cartoon [25] which is not part of this sample, but turned gold for all of Doom’s other non-comics appearances from that point onwards. Doom’s gun was first seen in the corpus as a whole in Fantastic Four #10 [26] although not in every panel, as shown in Fig. 4.3. The gun first appeared in the sample in Fantastic Four #85 [5] and returned sporadically from then on. For instance, it was not part of his costume for the Spider-Man cartoon series [27], but was present in the contemporaneous Spider-Man and His Amazing Friends series [28], and is also in The Amazing Spider-Man newspaper strip [29].

Fig. 4.3 Doom’s gun comes and goes in Fantastic Four #10

4

ANALYSIS

121

Doom’s tunic changed according to media type, being shown as a lighter shade in the 1967 cartoon series, and similarly 14 years later in Spider-Man and Spider-Man and His Amazing Friends , possibly as a way to differentiate between the cloak and character’s body in animation. Apart from these minor variations the central aspects of Doom’s appearance were thus highly coherent across both time and media. The fact that the development of his disk clasps took place at roughly the same time across different media could be seen as evidence that Doom is a transmedia character, as this evolution takes place in concert with other representations. On the other hand it might simply be that this coherence across time and media was due to intellectual property concerns, as the well-defined appearance of two-dimensional characters is what makes them so easy to copyright [30, 31] Whatever the reason, it is clear that this analysis demonstrates the coherence of Doom’s appearance across both time and media. Names and Titles Doom was referred to by a name or title in 60 of the 69 sample texts. Those where he was not named were all texts where he made brief cameo appearances, usually appearing silently in the background or in a montage. There were 37 different names and titles used overall. Table 4.2 shows all those that were used in four texts or more, with the percentage based on the 60 texts where a name or title appeared. Table 4.2 Names and titles by number of texts

Name/Title

Dr/Doctor Doom Doom Master Victor von Doom Monarch Doc Doom Doomsie Doctor von Doom Excellency Lord of Latveria Herr Doktor Master of the World Master of Menace Sire Tin-Head

Texts N

%

58 43 19 18 18 12 11 9 8 7 7 6 5 5 4 4

96.67 71.67 31.67 30 30 20 18.33 15 13.33 11.67 11.67 10 8.33 8.33 6.67 6.67

122

M. HIBBETT

As would be expected, Doctor Doom was called ‘Doctor Doom’ (or ‘Dr Doom’) in almost all texts where he was named. The two texts where he was not are Not Brand Echh #12 [32], where he plays ‘Baron von Doomenstein’, and What If ? #42 [33], where he is called ‘Von Doom’ throughout. The second most popular name was ‘Doom’ in 71.67% of texts, with other names all appearing less than half as often as the main two. Table 4.3 shows all texts where Doom was given a name or title by period (17 texts in the Creation period, 13 in Chaos and 30 in Consolidation), ordered by the overall popularity of each name/title. This shows that the main names/titles were set during the Creation period and then adhered to throughout the rest of this survey’s timespan. The major change was in the way that the use of less formal names—‘Doc Doom’, ‘Doomsie’ and variations on his full name without the title—increased during the Chaos period and then fell away slightly (but not completely) during the Consolidation period, although here ‘Tin-Head’ comes into use as well. This latter name appeared across different media during this time period, in Fantastic Four #198 [34], Fantastic Four Roast #1 [11] and episodes of The Amazing Spider-Man newspaper strip [29] and Spider-Man cartoon show [27], giving further weight to the idea that Doom evolved in a transmedia fashion. Table 4.4 shows that Doom was largely coherent across different media. He was called ‘Doctor Doom’ and ‘Doom’ in almost equal amounts, with the order ranking for names similar for comics and other media. He was never Table 4.3 Names and titles by period Name/Title

Dr/Doctor Doom Doom Master Monarch Victor von Doom Doc Doom Doomsie Doctor von Doom Excellency Lord of Latveria Herr Doktor Master of Menace Master of the World Sire Tin-Head

Creation

Chaos

Consolidation

N

%

N

%

N

%

16 12 6 5 3 3 1 1 1 4 1 2 3 1 3 0

94.12 70.59 35.29 29.41 17.65 17.65 5.88 5.88 5.88 23.53 5.88 11.76 17.65 5.88 17.65 0

13 9 4 4 6 4 4 4 3 1 2 2 2 0 0 0

100 69.23 30.77 30.77 46.15 30.77 30.77 30.77 23.08 7.69 15.38 15.38 15.38 0 0 0

29 22 9 9 9 5 6 4 4 2 4 2 0 4 1 4

96.67 73.33 30 30 30 16.67 20 13.33 13.33 6.67 13.33 6.67 0 13.33 3.33 13.33

4

ANALYSIS

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Table 4.4 Names and titles by media type Name/Title

Dr/Doctor Doom Doom Monarch Master Victor von Doom Doc Doom Doomsie Doctor von Doom Excellency Herr Doktor Lord of Latveria Master of Menace Master of the World Sire Tin-Head

Comics

Other

N

%

N

&

51 38 17 16 16 11 10 9 8 6 6 6 5 3 3 2

96.23 71.7 32.08 30.19 30.19 20.75 18.87 16.98 15.09 11.32 11.32 11.32 9.43 5.66 5.66 3.77

7 5 1 3 2 1 1 0 0 1 0 1 0 2 1 2

100 71.43 14.29 42.86 28.57 14.29 14.29 0 0 14.29 0 14.29 0 28.57 14.29 28.57

referred to as ‘von Doom’, ‘Herr Doktor’, ‘Lord of Latveria’ or simply ‘Doctor’ outside of comics, but was called ‘Master of the World’ or ‘Tin-Head’ proportionally much more often. These analyses show that Doom was referred to by many titles, but that the most popular ones remained in place across different media and time periods, having been set in place in comics media during the Creation period. However, where new names emerged they did so across media. Physical Actions This section concerns the physical actions that Doom displayed in 62 of the 69 texts. The seven texts missing are those where Doom had no physical actions displayed or reported, such as the ‘Rama Tut’ episode of the Hanna-Barbera Fantastic Four cartoon [24], where he only appeared as a headshot on a wall. There were 33 different physical actions recorded in total. Table 4.5 shows all those that appeared in 20% or more texts overall. The top result, ‘Dramatic pose/Power pose’ refers to situations where Doom was drawn in a non-natural stance intended to convey power or drama, as shown in Fig. 4.4. These examples also show Doom looming over other characters, giving orders and producing bolts of energy from his hands. These different signifiers were often combined, and were frequently used when Doom first appeared in a story, in order to establish his position and character.

124

M. HIBBETT

Table 4.5 Physical actions

Physical action

Dramatic pose/Power pose Looms over others Shakes or clenches fist/hand gestures Gives orders Bolts of energy from hands Watches from a distance/via screen Manipulates machinery Arms in the air (while speaking) Fighting/hand to hand combat Arms folded Strides (through crowds) Monologues

Texts N

%

39 32 29 29 27 25 23 21 18 17 15 15

62.9 51.61 46.77 46.77 43.55 40.32 37.1 33.87 29.03 27.42 24.19 24.19

Fig. 4.4 Doom striking power poses in Astonishing Tales #1 [35] and Dazzler #3 [36]

4

125

ANALYSIS

Indeed, almost all of the physical actions listed above denote power or mastery over a situation, such as striding through crowds, sitting on thrones or hitting underlings. Most of those that do not immediately indicate power involve him operating gadgets, whether that be manipulating machinery, building new items, using his jetpack or watching from afar on video screens, which are part of what Charles Hatfield calls ‘The Technological sublime’— ‘the use of high-tech motifs to represent vast forces’ [37]. These are thus also signifiers of power, originating in Jack Kirby’s work on Doom and then seen throughout the sample. Some physical actions that go against this theme appeared in around 10% of cases, including running away (always when defeated at the end of a story), falling unconscious, and mingling with other characters. This latter action almost always occurred either in flashbacks or in humour titles such as Not Brand Echh. The only exception to this was in Secret Wars II #7 [38] when Doom was mind-controlled by Mephisto, along with a crowd of other supervillains, in order to attack the Beyonder, and so was not really acting in character. Table 4.6 shows physical actions across time periods, again showing only those physical actions which appeared in 20% or more texts, and ranked by overall popularity. Table 4.6 Physical actions across time periods Physical action

Dramatic pose/ Power pose Looms over others Shakes or clenches fist/hand gestures Gives orders Bolts of energy from hands Watches from a distance/via screen Manipulates machinery Arms in the air (while speaking) Fighting/hand to hand combat Arms folded Strides (through crowds) Monologues

Creation

Chaos

Consolidation

N

%

N

%

N

%

12

70.59

11

78.57

16

51.61

12 9

70.59 52.94

6 8

42.86 57.14

14 12

45.16 38.71

8 5

47.06 29.41

8 9

57.14 64.29

13 13

41.94 41.94

10

58.82

7

50

8

25.81

8

47.06

6

42.86

9

29.03

7

41.18

5

35.71

9

29.03

4

23.53

6

42.86

8

25.81

4 4

23.53 23.53

5 3

35.71 21.43

8 8

25.81 25.81

7

41.18

1

7.14

7

22.58

126

M. HIBBETT

Table 4.7 Physical action signifiers per text, by period

Period

Mean physical actions per text

Creation Chaos Consolidation

6.65 7.5 5.6

This data shows that Doom’s character was also largely coherent over time, with the general order of signifier popularity staying roughly the same for each period, although there were differences of emphasis. For example, the power pose was the most common signifier across periods but looming over other characters was much more popular during the Creation period than either of other periods. In the Chaos period Doom was more than twice as likely to produce bolts of energy from his hands than during the Creation period but much less likely to monologue than during either of the other periods. During the Consolidation period Doom’s physical actions seem to even out more, with no single signifier being seen as often as in the other periods. Doom also exhibits fewer physical action signifiers per text in this period, as Table 4.7 shows: This also shows that Doom’s character continued to develop from the Creation to Chaos period, with a wider variety of physical actions, but then solidified into a smaller group of actions during Consolidation. This would also explain why the signifiers that were displayed were less varied in this period—the definition of Doom’s character had solidified, in this dimension at least. Table 4.8 shows physical actions split into comics and other media, ranked in order of overall popularity. As above, only those which appeared in 10% or more texts overall are shown. This data shows that Doom’s physical actions were, for the most part, coherent across all media. In the top half of the table the ordering of actions is very similar for comics and non-comics, only differing to any great extent when it reaches ‘Fighting/hand to hand combat’ which did not appear at all in other media despite being the joint eighth most common signifier for comics. Similarly Doom did not rant or use a jetpack in other media, but on the other hand was much more likely to sit on a throne, invent gadgets, or run away from trouble. Put together this suggests that the Doom seen in other media was much less hands-on than in comics, and less likely to become involved in physical conflict, seeking to avoid it if possible. Overall, Doom’s characteristics in this dimension varied slightly over time and media, but were generally focused on three areas—power, distance and inventiveness. The idea of ‘power’ was demonstrated by his power poses, the way he looms over other characters, shakes his fist angrily, and gives orders, among many

4

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127

Table 4.8 Physical actions by media type Physical action

Dramatic pose/Power pose Looms over others Gives orders Shakes or clenches fist/hand gestures Bolts of energy from hands Watches from a distance/via screen Manipulates machinery Arms in the air (while speaking) Fighting/hand to hand combat Arms folded Monologues Strides (through crowds) Close-up of eyes Rants/Shouts Flies/hovers (often with jetpack) Sits on Throne Builds/invents things Hits someone (usually an underling) Runs away Mingles in crowd of characters Unconscious Removes mask Slamming hand on table/surface Stands alone on battlements/balcony

Comics

Other

N

%

N

%

34 29 25 26 23 21 20 18 18 15 11 14 13 12 11 8 5 8 6 7 7 7 5 6

61.82 52.73 45.45 47.27 41.82 38.18 36.36 32.73 32.73 27.27 20 25.45 23.64 21.82 20 14.55 9.09 14.55 10.91 12.73 12.73 12.73 9.09 10.91

5 3 4 3 4 4 3 3 0 2 4 1 1 0 0 3 4 1 3 1 1 0 2 1

71.43 42.86 57.14 42.86 57.14 57.14 42.86 42.86 0 28.57 57.14 14.29 14.29 0 0 42.86 57.14 14.29 42.86 14.29 14.29 0 28.57 14.29

others. Whatever the time period or media these aspects of his physical actions were always the ones most often demonstrated. This power was often, though not always, exerted from a distance, most commonly through the use of view screens, operated from command centres and throne rooms. Doom was still willing to get involved physically with other characters—whether that be by using power bolts or engaging in fighting—but not as often. Finally, Doom was involved with machinery, whether manipulating existing gadgets (usually, as above, from a distance) or by inventing new ones during the course of an adventure. In summary then, Doom acted powerfully, usually but not always from a distance, and with the help of machines and devices.

128

M. HIBBETT

Dialogue This section concerns the 47 texts where Doom had dialogue. There were more missing texts than usual for this dimension because there were so many texts where Doom did not say anything at all, such as when he appeared in a background cameo, a poster image or a teaser for the next issue. Table 4.9 shows all of the items of dialogue that appeared in 10% or more texts where any dialogue was present, overall and by time period. The most common signifiers here were not specific words or phrases, but rather the way that Doom spoke, i.e. formally and in the third person, praising himself and referring to himself as ‘Doom’ in some way. For clarity, the use of the specific phrase ‘I am Doom’ was recorded separately as it was repeatedly mentioned as important during the respondent survey, so ‘Other references to self as Doom’ is a catch-all for any other uses of this form of his name. The word ‘Richards!’ was also included as a result of the respondent survey. There were large differences over time between the use of different terms. Speaking in the third person rose from just 64% of texts during Creation to 100% by the time of Consolidation, while his tendency towards selfaggrandisement almost halved, from 78 to 45%. Put alongside similar results in other dimensions, this shows the development of Doom’s character. For example, his speech was often much less formal in his earliest appearances than Table 4.9 Dialogue overall and by time period Overall

Creation

Chaos

Consolidation

Dialogue

N

%

N

%

N

%

N

%

Speaks in third person Formal tone Other references to self as Doom General self-aggrandisement Fool(s)! Bah! You dare? Richards! Dolt I am Doom My dear Silence! Wha?? You are helpless Hah!

40

85.11

9

64.29

9

81.82

22

100

39 30

82.98 63.83

11 9

78.57 64.29

10 9

90.91 81.82

18 12

81.82 54.55

28

59.57

11

78.57

7

63.64

10

45.45

22 13 10 9 8 8 8 7 7 6 5

46.81 27.66 21.28 19.15 17.02 17.02 17.02 14.89 14.89 12.77 10.64

6 6 3 3 2 2 3 1 3 4 5

42.86 42.86 21.43 21.43 14.29 14.29 21.43 7.14 21.43 28.57 35.71

6 4 3

54.55 36.36 27.27 0 27.27 36.36 27.27 27.27 0 0 0

10 3 4 6 3 2 2 3 4 2 0

45.45 13.64 18.18 27.27 13.64 9.09 9.09 13.64 18.18 9.09 0

3 4 3 3 0 0 0

4

ANALYSIS

129

100 90 80 70 60 50 40 30 20 10 0 Command

Exclamation Creation

Chaos

Ingratiating

Insult

Consolidation

Fig. 4.6 Dialogue groupings by period

would be expected later on, while the drop in self-aggrandisement occurred as he became more complex and more prone to self-doubt and inner turmoil. In order to assess the frequency of these categories all signifiers were grouped together as commands, exclamations, attempts at ingratiation and insults, as shown in Fig. 4.5. This shows that Doom was much more likely to use an insult (such as ‘Fool’, ‘Dolt’, ‘Clod’ or ‘Cretin’) than any other type of phrase. Exclamations (such as ‘Bah!’ or ‘Hah’) featured in just over half of texts, while commands and ingratiating terms (such as ‘my dear’) appeared in just over 20% of texts each. However, although the forms of speech he used in this way changed over time, the balance of grouped word-types used (once again summarised as commands, exclamations, ingratiations or insults) was generally the same across time, as shown in Fig. 4.6. 100 90 80 70 60 50 40 30 20 10 0 Command

Exclamation

Ingratiating

Fig. 4.5 Frequency of recoded dialogue groupings

Insult

130

M. HIBBETT

Insults were always the phrase-type most often used, followed by exclamations, with ingratiating remarks and commands more or less equal. However, in most cases such words became much less prominent over time. Doom became more insulting during the Chaos period, but other than that there are fewer of all types of words used over time. It could be that such words were used more often in the earlier period because writers then tended to use dialogue as a way to define character more than those in later periods, where other methods such as appearance and actions became more important. It could also indicate a change in writing style, with Stan Lee’s distinctive dialogue style being replaced by something less verbose as time goes by. Examining dialogue across different media is difficult because there were only four texts in non-comics media where Doom had any dialogue. This was mostly due to the fact that, in the texts included in the sample at least, Doom was often used as a background or cameo character who did not speak, especially in the Marvel Superheroes Secret Wars role playing game [39] which did not feature any dialogue at all. The top six dialogue characteristics were the same across media and appear in the same order, but more detail became available by once again recoding the phrases, as shown in Fig. 4.7. This shows that Doom was remarkably consistent across media, with very similar frequencies for these types of dialogue in both comics and other media. Overall, Doctor Doom spoke formally, in the third person, and liked to praise himself. As time went on he became more likely to use the third person and less likely to praise himself, but these changes occurred across time rather than across media, and so can be regarded as character development rather than a lack of transmedia coherence. The specific words and phrases he used were most likely to be insults, followed by exclamations, and he also attempted to ingratiate himself with other characters or give them commands. These aspects were also coherent across time and media, although were less frequently used overall as time went on. 100 90 80 70 60 50 40 30 20 10 0 Command

Exclamation Comics

Fig. 4.7 Dialogue by media type

Ingratiating Other

Insult

4

ANALYSIS

131

In conclusion, although Doom’s dialogue did change slightly in some ways as the character developed, for the most part it can be considered as coherent throughout. Summary of Character-Specific Components Using this methodology allowed for an in-depth analysis of specific changes, or lack of changes, over time and media for each dimension, which in turn drew out evidence of how the character developed. Many of these aspects might not have been evident if examined qualitatively. For instance, the stability of Doom’s appearance over both time and media might be deduced by simply reading the texts, but using this tool gives statistical proof that it happened. Similarly, the grouping of Doom’s characteristics into the areas of power, distance and inventiveness could be identified from a conventional close reading of the sample, but the statistical measures employed here allow their changes to be mapped over time. Taking all of the analyses together a clear picture emerges of Doom during this period as a coherent character whose character-specific signifiers remained broadly stable across both time and media. That is not to say that the character does not develop—for example, his tendency to speak in the third person becomes more marked over time—but instead that this development tended to happen uniformly across media. This can thus be described as transmedia character development, rather than incoherence in the portrayal of the character. Again, using this methodology is a way of demonstrating this empirically. In conclusion, analysis of these character-specific components shows firstly that the tool functions well as a measure of character coherence, and that, for this group of components, Doom himself was largely coherent across both time and media.

Analysis of Storyworld-Specific Components This next group of components refers to signifiers of Doom’s storyworld, including other characters, objects, settings and previous events. During the data entry for these components it was noted whether Doom interacted with each signifier. In many of the sample texts there were stories which did not relate to Doom at all, especially on occasions when he appeared as an isolated cameo. All signifiers in the text were still recorded, but by noting Doom’s interaction it was possible to make a distinction between the storyworld that Doom carried with him and the wider shared storyworld that his stories took place in. An example of this can be seen in Thor #271 [40]. Here Doom made a single panel appearance standing on the battlements of his castle, musing on events happening elsewhere. In the same text there was also a single panel showing Stilt Man as part of a recap of the previous issue. This had nothing to do with Doom’s appearance, and was the only time that the two characters

132

M. HIBBETT

appeared in the same text anywhere in the sample. Similarly, the only time a SHIELD Heli-carrier was used as a location within the sample featured in this issue, but Doom was not present. Stilt Man and SHIELD Heli-carriers are aspects of the wider storyworld which Doom existed in, but here they were not signifiers of Doom himself. Thus, recording this interaction allows for data to be presented both as it relates to the overall text and also to the specific character. Other Characters Overall there were 328 separate characters who appeared in the same texts as Doctor Doom, with over half (169) only appearing in a single text and only 42 appearing in 10% or more texts. Almost all of these were named characters, as opposed to background figures, with a few exceptions that were coded during data entry into Marvel-specific categories such as ‘Latverian peasants’ or ‘Latverian resistance’. The inclusion of so many specific characters, all of whom would appear in other series, demonstrates the sheer scale of the ‘Marvel Universe’ storyworld. There were 195 characters who interacted with Doctor Doom, and again slightly over half (101) only made a single appearance, with only 16 appearing in 10% or more texts. Overall, there was an average of 16.75 characters per text overall, with Doom interacting with an average of 9.34. The fact that over a third of characters did not interact with Doom was partly due to his use as a supporting character, often appearing as just one part of a multi-plot story, or as a cameo, often in a single frame isolated from the rest of the story as in the example from Thor #271. Those characters that interacted with in at least 10% of texts are shown in Table 4.10. All texts had other characters in them, so percentages are based on all 69 texts, including the eight texts where Doom did not interact with anybody. The most surprising aspect of this table is that it is The Thing who Doctor Doom interacted with the most rather than Mr Fantastic, despite the fact that the respondents’ survey strongly suggested that Reed Richards was Doom’s most important adversary. This result was mostly due to the fact that The Thing had two series of his own during ‘The Marvel Age’, Marvel Two-in-One and The Thing , both of which Doom appeared in. Away from the Fantastic Four, Captain America was the most frequently interacted-with character, appearing more often even than Namor the SubMariner, with whom Doom shared the series Super-villain Team-Up. The members of Doom’s own supporting cast who were mentioned frequently by respondents to the Signifier Survey—characters such as Boris, Valeria and Doom’s mother—do not feature in this table, which again is due to the fact that Doom was mostly used as a guest character, rather than in a

4

Table 4.10 Other characters interacted with by number of texts

Other characters

Thing Mr Fantastic Human Torch Invisible Girl Captain America Latverian Lackey Iron Man Namor the Sub-Mariner Spider-Man Hulk Latverian Peasants Galactus Silver Surfer Thor Daredevil Magneto

ANALYSIS

133

Texts N

%

28 24 23 19 19 14 13 13 11 11 10 8 8 8 8 7

40.58 34.78 33.33 27.54 27.54 20.29 18.84 18.84 15.94 15.94 14.49 11.59 11.59 11.59 11.59 10.14

leading role. Boris did appear in 5 texts within the sample, but Valeria did not appear at all. Table 4.11 divides the same data into periods, and shows that the characters Doom interacted with most changed dramatically over time. During the initial Creation period of the Marvel Age Doctor Doom was at least twice as likely to interact with members of The Fantastic Four as with other characters, with Namor, Daredevil and the Silver Surfer being the next most popular. However, during the Chaos period Doom interacted most often with Captain America, largely due to a series of guest appearances in SuperVillain Team-Up. Scarlet Witch and The Vision were in second place because of their appearances in Marvel Team-Up, alongside Iron Man, while Namor was also in second place due to the fact that he and Doom shared the SuperVillain Team-Up title. The Fantastic Four were reduced to tenth place along with characters such as Spider-Man, The Hulk , and Latverian peasants. This is all related to Doom’s change to a protagonist in his own series during this time and the resulting drop in appearances as the villain in The Fantastic Four. By the Consolidation period, however, he had returned to his primary role as antagonist, but here his interactions were shared between the Fantastic Four, Spider-Man, the Hulk and various members of the Avengers as he roamed more widely again. This again demonstrates that his role developed over time, as also shown in other dimensions. Figure 4.8 shows the average number of other characters per text by media period, divided into those that Doom did and did not interact with.

134

M. HIBBETT

Table 4.11 Other characters by period Other characters

Creation

Thing Mr Fantastic Human Torch Invisible Girl Captain America Latverian Lackey Iron Man Namor the Sub-Mariner Spider-Man Hulk Latverian Peasants Galactus Silver Surfer Thor Daredevil Magneto

Chaos

Consolidation

N

%

N

%

N

%

11 12 10 8 4 5 3 6 1 4 4 3 6 3 5 1

55.00 60.00 50.00 40.00 20.00 25.00 15.00 30.00 5.00 20.00 20.00 15.00 30.00 15.00 25.00 5.00

2 1 1 2 6 1 3 5 2 1 2 0 0 0 0 1

14.29 7.14 7.14 14.29 42.86 7.14 21.43 35.71 14.29 7.14 14.29 0.00 0.00 0.00 0.00 7.14

15 11 12 9 9 8 7 2 8 6 4 5 2 5 3 5

42.86 31.43 34.29 25.71 25.71 22.86 20 5.71 22.86 17.14 11.43 14.29 5.71 14.29 8.57 14.29

25 20 15 10 5 0 Creation

Chaos Overall

Consolidation

Interacts with

Fig. 4.8 Other characters per text by period

This graph shows that the balance between the number of characters that Doom did or did not interact with in a text remained roughly the same during the Creation and Chaos periods, with slightly more characters for each category in Creation than Chaos, and that in each period Doom interacted with between 60 and 70% of all of the characters in a text. However, this changed dramatically during the Consolidation period, when the average number of characters in a text increased substantially while the number Doom interacted

4

ANALYSIS

135

with remained about the same. On average he interacted with 8.7 characters per text during Consolidation, compared to 9.2 in Creation and 6.5 in Chaos. This increase is partly due to a few texts which had a very large number of characters, such as Fantastic Four Roast and the Marvel Superheroes Secret Wars series, skewing the figures somewhat. It is also due to the fact that more and more characters became available as the Marvel Universe itself expanded over time, demonstrated by the graph in Fig. 4.9. Here the blue bars show the actual number of different characters that Doom interacted with per period, while the orange bar shows the number of characters once it has been adjusted to take into account the number of texts, i.e. it shows the number of characters that would be expected if the number of texts in the Creation and Chaos periods was the same as in Consolidation. This shows that although the actual number of different characters that Doom interacted with did rise over time, showing the expansion of the storyworld, the adjusted figure was much higher during the middle Chaos period. This is once again indicative of his additional status as a protagonist during this time, not just appearing as a villain in other series but requiring a supporting cast and villains of his own. Next, Fig. 4.10 shows the average number of other characters per text by period, divided into those that Doom did and did not interact with. This graph shows that although the number of different characters was higher overall during the Chaos period, the number of characters per text was lower than in the other two, whether that was characters that Doom interacts with or not. As with other charts, it also shows that there were much more characters in the Consolidation period than others, which again is due to the increased size of the universe and the outlier texts with huge casts, but here we can see that on average Doom actually interacted with slightly fewer characters per text in this period than he did during the earlier Creation. The number of characters interacted with per text ranged from 6.5 during Chaos 400 350 300 250 200 150 100 50 0 Creation

Chaos Actual

Consolidation

Adjusted

Fig. 4.9 Adjusted number of different characters featured per period

136

M. HIBBETT

25 20 15 10 5 0 Creation

Chaos Overall

Consolidation

Interacts with

Fig. 4.10 Average number of characters per text by interaction and period 100 90 80 70 60 50 40 30 20 10 0 Creation 0

Chaos 1-5

6-10

16-20

Consolidation 11-15

>20

Fig. 4.11 Grouped number of characters per text by period

to 9.15 during Creation, which is not a huge difference compared to how many characters appear in the texts altogether. Figure 4.11 shows the number of characters Doom interacted with in each text put into groupings, so that it shows the number of texts where he interacted with 1–5 characters, 6–10 and so forth. This shows that, generally speaking, there was not a huge difference in the number of characters Doom interacted with, and in 75% of cases in each period he interacted with between 1 and 10 characters. The only major change over time was the increase in texts where he did not interact with any other characters at all, probably due to him being used more often as a cameo. These results show that although Marvel’s storyworld was changing and growing over time, with Doom meeting a wider range of characters, the number he interacted with per text remained stable. To put it another way,

4

ANALYSIS

137

the basic structure of stories involving Doom, including the number of cast members, stayed stable, while the cast members themselves changed over time. Indeed, as time passed he came to be used as a ‘senior’ character, who newer characters would at some point have to be introduced to in order to affirm their membership of the Marvel storyworld. Moving on to look at this data in terms of media type, Table 4.12 shows all characters who appeared in at least 10% of texts overall, split into comics and other types of text. The frequency of appearances for characters is very similar in comics and non-comics texts throughout, although in some cases the smaller sample size for non-comics media makes it appear as if characters appeared much more often there than they did in comics. For example, The Beast appears in 4 comics (6.56% of that part of the sample) but also 25% of other media, which is one text. The only character who appeared in enough texts across different media to make these figures significant was Spider-Man, who appeared in only 13.11% of comics texts but 37.5% of other media, due to Doom’s appearances in both of the Spider-Man cartoon series as well as the newspaper strip. The most popular character across both types of media was still The Thing, demonstrating that his placement at the top of the overall list was not entirely due to his range of solo series. Here his ranking above the rest of the Fantastic Four is due to the fact that he appears in the Power Records adaptation [41] of Fantastic Four #126 [42] along with The Invisible Girl, and also the Secret Table 4.12 Other characters by media type Other characters

Thing Mr Fantastic Human Torch Invisible Girl Captain America Latverian Lackey Iron Man Namor the Sub-Mariner Hulk Spider-Man Latverian Peasants Galactus Silver Surfer Thor Daredevil Magneto

Comics

Other

N

%

N

%

24 21 20 16 17 12 10 12 10 8 8 7 8 6 7 6

39.34 34.43 32.79 26.23 27.87 19.67 16.39 19.67 16.39 13.11 13.11 11.48 13.11 9.84 11.48 9.84

4 3 3 3 2 2 3 1 1 3 2 1 0 2 1 1

50 37.5 37.5 37.5 25 25 37.5 12.5 12.5 37.5 25 12.5 0 25 12.5 12.5

138

M. HIBBETT

Wars role playing game handbook [39], which includes Mr Fantastic and The Human Torch, but not The Invisible Girl. There was only one character—The Silver Surfer—who interacted with Doom in at least 10% of comics texts yet did not appear in any other media. Overall there were 117 characters who interacted with Doom only in comics, but the vast majority of these (85) made only one appearance, and there were only six characters apart from the Silver Surfer who appeared four or more times in comics but never in other media. These were Prince Rudolfo, The Shroud, Scarlet Witch, Vision, The Red Skull and Wolverine. There were only four characters who appear in other media, but not in comics. These were mostly made up of background characters who appeared in the Secret Wars role playing game handbook or as other costumes displayed next to Doctor Doom in the costume shop in The Amazing Spider-Man cartoon series. However, most of these characters were also present in other Doom comics of the period which were not included in the sample. The only character who appeared in non-comics media but never appeared in comics anywhere in the rest of the corpus is Kitty, the reporter who accompanies Peter Parker to Latveria in The Amazing Spider-Man newspaper strip. Figure 4.12 shows the average number of other characters per text by media type, divided into those that Doom did and did not interact with. This graph shows that there were slightly more characters per text overall in comics than in other media, but that Doom interacted with many more characters in other media. One might expect that there would have been more characters in comics than in other media, as comics had the entire Marvel storyworld to use whereas licensing issues would reduce the number available to other media, so it is surprising to see that the numbers were almost the same. This demonstrates that the Marvel storyworld was widely used across media, suggesting that it was an early example of transmedia, and Doom a transmedia character. 18 16 14 12 10 8 6 4 2 0 Comics

Other Overall

Fig. 4.12

Interacts with

Average number of characters per text by interaction and media type

4

ANALYSIS

139

The fact that Doom interacted with almost twice as many characters per text in other media than in comics could be explained by him being used more often as the main antagonist in these stories and less as a cameo. Also, the space constraints of some non-comics texts such as newspaper strips would have required a more linear form of storytelling, with a single storyline involving all featured characters rather than several different ones occasionally interacting. However, as Fig. 4.13 illustrates, it is also due to the nature of the sample. This graph shows that around 40% of texts in both comics and non-comics had Doom interacting with 0–5 other characters, and around 70% with up to 10 characters. However, after that all other non-comics texts had at least 20 other characters, whereas there is a gentler increase for comics. This, added to the fact that there are no non-comics texts where Doom did not interact with any other characters, explains why the average is higher here—if the sample size were larger it is possible that there would be texts where Doom did not interact with other characters. Having said that, my viewing of the entire corpus did not reveal a single text where Doom failed to interact with any other characters. These results show that Doom’s interaction with other characters is generally stable across media but changes over time. There was only one prominent character in comics, the Silver Surfer, who did not appear in other media in the sample, and only one character in noncomics media, Kitty the reporter, who did not appear in any comics at all. The use of other characters across media was broadly similar, with the only big difference being that Spider-Man appeared more often in Doom’s stories outside comics than in them, which can be put down to the fact that SpiderMan had more outlets in other media. Doom did interact with a larger number of characters per text on average in other media, but this was mostly due to the fact that he was used more prominently than in comics, and less often in a cameo. 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Comics 0

Fig. 4.13

1-5

Other 6-10

16-20

11-15

>20

Grouped number of characters per text by media type

140

M. HIBBETT

As in other categories, this demonstrates that Doom’s character and stories did develop a little during The Marvel Age, but that these changes happened across media types, giving further evidence for the argument that he is an early transmedia character. He is also shown to be an important character within the overall storyworld, being used with more and newer characters as the Marvel universe expanded, an expansion which is evidenced by this analysis. Objects There were thirty different objects identified as part of the data entry process, and these appeared in 65 of the 69 sample texts. Those which appeared in 10% of texts or more are shown in Table 4.13. Almost all of these objects can be grouped together as ‘technology’ of some sort, often employing aspects of Kirbytech even when not drawn by Kirby himself. This was especially the case in John Byrne’s long run on Fantastic Four from Fantastic Four #232 [43] to Fantastic Four #294 [44] where Byrne sought to both acknowledge Kirby’s influence and simultaneously stake a claim to surpassing him [45]. Table 4.13 Objects by number of texts Objects

Machinery/ Kirbytech Viewing screen High-tech weaponry Control console Doombots/ Robots Gauntlet blaster Throne Rocket pack Force field projector Silver Surfer’s Board Fantasti-car Artworks

Overall

Creation

Chaos

Consolidation

N

%

N

%

N

%

N

%

45

69.23

16

80

9

69.23

20

62.5

35

53.85

14

70

7

53.85

14

43.75

34

52.31

14

70

9

69.23

11

34.38

28

43.08

10

50

10

76.92

8

25

18

27.69

5

25

2

15.38

11

34.38

18

27.69

0

0

7

53.85

11

34.38

14 12 8

21.54 18.46 12.31

7 3 0

35 15 0

5 3 5

38.46 23.08 38.46

2 6 3

6.25 18.75 9.38

7

10.77

5

25

0

0

2

6.25

7 7

10.77 10.77

3 1

15 5

1 0

7.69 0

3 6

9.38 18.75

4

ANALYSIS

141

The specific item which was most often employed across all texts was the viewing screen. This was only mentioned by two of the fan-survey respondents and, as stated previously, this may be due to the fact that most based their responses on modern stories, where viewing screens are a feature of daily life, rather than the science fiction idea that they would have been during the period of the sample. Other items noted can mostly be grouped into two other categories. The first category is magic or cosmic items, such as the Silver Surfer’s board, magical artefacts and books of magic, while the second is signifiers of Doom’s status as royalty, such as artworks, goblets and especially his throne. ‘Magic/ Dark Arts’ was one of the most mentioned of Doom’s physical actions in the fan survey, but here it seems that magic was much less likely to be shown than technology, at least in terms of objects, even though the combination of both is a key part of Doom’s origin story. Doom’s main object signifiers were also fairly coherent across time, with the same six objects remaining at the top of the rankings in each period, these being Machinery/Kirbytech, viewing screens, high-tech weaponry, control consoles, doombots/robots and the rocket pack. There were however some differences between the periods. The Fantasticar appeared more often and was more highly ranked in the Creation period than others, because Doom was more likely to appear in stories with The Fantastic Four during that time. His gauntlet blasters (that is, the blasters in the palms of his hands, rather than simply the gauntlets that form part of his costume) were not used at all during the Creation period, but rose to become the fifth most noted objects in both Chaos and Consolidation. Although not as popular, Doom’s force field projector also first appeared in Chaos and was continued into Consolidation. The larger changes occurred in the Consolidation period. Here the use of Doom’s throne almost stopped, while artwork became much more popular. Also during this period magical artefacts and books of magic were much more likely to appear, while mad science devices (often the driving force for Doom’s quests) became significantly less important. Put together this all suggests a change in the way Doom was portrayed, less as a King who uses technology, and more as a powerful individual who uses magic. Table 4.14 demonstrates that the transmedia ranking of objects was also very coherent, with seven of the top ten objects in comics in the top ten for non-comics, and vice versa. The main differences are that the Silver Surfer’s board did not appear at all in the non-comics sampled, while objects denoting Doom’s royal status, such as his throne or artworks, were much more likely to appear in non-comics than comics. The absence of the surfer’s board was due to his story not being retold in other media during this period, although it would be adapted for later adaptations such as Fantastic Four: The Animated Series [46] and Fantastic Four: Rise of the Silver Surfer [47]. The increased use of Doom’s royal objects shows

142

M. HIBBETT

Table 4.14 Objects by media type Objects

Machinery/Kirbytech Viewing screen Weaponry (high tech) Control console Doombots/Robots Gauntlet blaster Throne Rocket pack Force field projector Silver Surfers Board Fantasti-car Artworks

Comics

Other

N

%

N

%

39 30 29 22 15 15 9 11 5 7 6 4

68.42 52.63 50.88 38.6 26.32 26.32 15.79 19.3 8.77 12.28 10.53 7.02

6 5 5 6 3 3 5 1 3 0 1 3

75 62.5 62.5 75 37.5 37.5 62.5 12.5 37.5 0 12.5 37.5

how this aspect of his character was more of a focus outside of comics, where his status as the leader of an independent country was more often the source of storylines. Doom’s royal status here is used to signify his overall power in a way that would have been comprehensible to the wider audience consuming non-comics texts. Such audiences would not necessarily be familiar with the importance of the sort of magical or cosmic signifiers of power that appeared in comics texts as a matter of course, but would be instantly familiar with the idea that political power, such as ruling a country, could be a source of threat. As with other storyworld signifiers, the type of objects related to Doom were largely coherent over time and media. There was some development over time, as new objects were introduced, and some differences of emphasis between comics and other media due to the types of storylines which appeared, but Doom was always associated with high-tech gadgets of some kind and, to a lesser extent, signifiers of his powerful status. Settings This section deals with the times or places in which Doom’s stories were set. In this sample 68 of the 69 texts had some sort of setting, with the only exception being Crazy Magazine #82 [48], where Doom is shown fighting the Hulk in a deliberately unspecified white space. Other than that example, settings could be identified for all texts, and so percentages given in the following tables will be based on the 68 where this was the case. Table 4.15 shows that the top three settings are what would be expected for Doctor Doom, with New York as the main location for stories told in

4

143

ANALYSIS

the Marvel storyworld, while Latveria and Castle Doom were identified in the survey as key signifiers for Doom. Most other settings, apart from the Baxter Building, Doomstadt and Avengers Mansion, are non-specific, covering general control rooms, dungeons, throne rooms and so forth rather than individual places. Similarly settings such as ‘Space’ covers all adventures outside of Earth’s atmosphere, while ‘Countryside’ refers to any rural setting. Settings such as these were part of Doom’s ‘mise en scéne’, used as backdrops to convey meaning about his activities, rather than as specific places [49]. For instance, the use of a ‘laboratory’ as a setting suggests that science (possibly Mad Science) is in play, while a ‘throne room’ conveys the regal or political power mentioned in the previous section. As with other storyworld dimensions, data entry here included an ‘interaction’ tag, recording whether or not Doctor Doom actually appeared in the settings. For example, in Silver Surfer #1 [50] Doom was shown only in flashback, sitting in Castle Doom. This was later specified as being in Latveria, so both ‘Castle Doom (Latveria)’ and ‘Latveria’ were recorded as being interacted with. The rest of the issue saw the Surfer visiting space, hell, the Baxter Building and various other places which were recorded as being part of the Table 4.15 Settings that appear in at least 10% of texts Settings

New York City/Manhattan Castle Doom (Latveria) Latveria Laboratory Control Room Baxter building Space Countryside Aircraft Dungeon Ocean Other dimensions/realms Throne room Mountains Underground Doomstadt Battleworld United Nations Building Avengers Mansion

Texts overall

Texts where Doom interacts

N

%

N

%

37 27 27 26 21 20 19 15 13 12 11 11 10 10 8 7 7 7 7

53.62 39.13 39.13 37.68 30.43 28.99 27.54 21.74 18.84 17.39 15.94 15.94 14.49 14.49 11.59 10.14 10.14 10.14 10.14

19 27 25 16 18 10 7 6 5 11 6 4 8 3 4 6 6 6 0

27.54 39.13 36.23 23.19 26.09 14.49 10.14 8.7 7.25 15.94 8.7 5.8 11.59 4.35 5.8 8.7 8.7 8.7 0

144

M. HIBBETT

text but, as Doctor Doom is not shown in those settings, not interacted with. The setting ‘Castle Doom (Latveria)’ was so-called to distinguish it from the other Castle Doom located in upstate New York which appeared five times in the sample, although Doom only interacted with it three times. Chronological settings ‘The Past’ and ‘The Future’ were included in the analysis, with all other settings taking place in the present day as it was when the text was published. ‘The Past’ appeared four times, with three interactions from Doom, while ‘The Future’ appeared twice without Doom interacting. Other more specific time periods could be used for characters who either inhabit a different time period, such as Morgana Le Fey, or use time travel regularly like Kang The Conqueror. Doom does travel through time and indeed owns a time machine, but this did not feature regularly in the texts selected for the sample here. New York appeared in most texts overall, but Doom interacted more often with Castle Doom and Latveria itself. As stated above, New York was and is the main setting for most of the Marvel storyworld, but often appeared in the sample as part a separate storyline in a text apart from the one featuring Doom. Similarly, Doom did not interact with Avengers mansion at all, despite it appearing seven times in the sample as a whole. In contrast, dungeons and throne rooms, which Doom did regularly interact with, ranked higher for interactions than when all texts were included. The next set of tables in this section look at settings by period. Table 4.16 shows the top ten most popular settings overall, with figures for all settings for each of the three periods Creation, Chaos and Consolidation. These figures show that the Marvel storyworld, or at least the parts of it contained within the sample, was fairly stable in terms of settings. New York Table 4.16 Settings by period Settings

New York City/Manhattan Castle Doom (Latveria) Latveria Laboratory Control Room Baxter building Space Countryside Aircraft Dungeon Ocean

Creation

Chaos

Consolidation

N

%

N

%

N

%

11 8 9 5 5 10 3 3 4 4 2

55 40 45 25 25 50 15 15 20 20 10

8 5 4 5 7 2 4 3 2 3 4

57.14 35.71 28.57 35.71 50 14.29 28.57 21.43 14.29 21.43 28.57

18 14 14 16 9 8 12 9 7 5 5

52.94 41.18 41.18 47.06 26.47 23.53 35.29 26.47 20.59 14.71 14.71

4

145

ANALYSIS

remained the most common in all periods, with Latverian settings not far behind and then Doom’s centres of technology behind those. The Baxter Building featured much more prominently in the Creation period, which is indicative of Doom’s beginning as a Fantastic Four villain before branching out into his own stories later on. Apart from that the use of settings, and especially the ranking of settings, was fairly similar in Creation and Consolidation periods, with Chaos differing more from both. Here Doom’s control room was the second most used setting, while Castle Doom and Latveria were seen less often. Ocean settings were also more common, which was due to the run of texts featuring Namor The Sub-Mariner during this period. These changes can be seen even more clearly by looking at the settings that Doom specifically interacted with during each period, shown in Table 4.17. As before, New York dropped down the ranking of settings when Doom’s interaction was taken into account. Doom’s interaction with this setting also dropped off over time, demonstrating that it must have been appearing in storylines set apart from Doom’s. For instance, one of the various sub-plots in Avengers #155 [10] concerned Wonder Man, Beast and The Whizzer investigating matters in New York, but this plot did not interact with Doom’s in any way during this text, and nor did the setting. In this way the tool can be used Table 4.17 Settings interacted with by period Settings

Castle Doom (Latveria) Latveria New York City/Manhattan Control Room Laboratory Dungeon Baxter building Throne room Space Battleworld Countryside Doomstadt Ocean United Nations Building Aircraft Command Ship Other dimensions/realms Underground Atlantis

Creation

Chaos

Consolidation

N

%

N

%

N

%

8 9 7 4 3 4 7 4 1 0 3 2 2 0 3 0 1 1 1

40 45 35 20 15 20 35 20 5 0 15 10 10 0 15 0 5 5 5

5 4 4 6 3 2 1 2 1 0 2 0 1 1 0 2 1 1 1

35.71 28.57 28.57 42.86 21.43 14.29 7.14 14.29 7.14 0 14.29 0 7.14 7.14 0 14.29 7.14 7.14 7.14

14 12 8 8 10 5 2 2 5 6 1 4 3 5 2 2 2 2 1

40 34.29 22.86 22.86 28.57 14.29 5.71 5.71 14.29 17.14 2.86 11.43 8.57 14.29 5.71 5.71 5.71 5.71 2.86

146

M. HIBBETT

to give evidence of a change of style in storytelling over time, from simpler linear stories in the earlier period to more complex multiple plotlines later on. Another change during the Chaos period was that control rooms become the most interacted with setting, even above Castle Doom and Latveria. During the Consolidation period Doom interacted more with the United Nations building, space, and especially Battleworld. Battleworld did not appear at all until the Secret Wars series but then became a key setting for Doom. This was partly because he was the de facto lead character in that series, but also because Secret Wars and its setting was then referred to so often in flashbacks in subsequent texts as a previous event signifier. Comparing media types for settings gives a slightly distorted effect due to the low number of texts in the non-comics category. For example, a glance at the data seems to show that Micro-World was eight times as likely to appear in a non-comic than a comics text, purely because the same story appeared in both media. With this in mind it is more informative to rank the settings, as shown in Table 4.18. Ranking the settings in this way shows more clearly the differences between comics and non-comics texts in the sample. Castle Doom appeared much less often in non-comics while laboratories appeared more, showing a slight shift Table 4.18 Ranked settings by media type

Settings

Comics rank

Other rank

Castle Doom (Latveria) Latveria Control Room New York City/Manhattan Laboratory Dungeon Baxter building Throne room Countryside Space Battleworld Doomstadt Ocean United Nations Building Other dimensions/realms Underground Aircraft Command Ship Atlantis Soviet Russia White House Doom Island Micro-World

1 2 3 4 5 6 7 8 8 10 10 10 10 14 14 14 17 17 19 19 19 22 22

6 3 3 1 1 6 3 6 6 12 12 12 6 6 12 12 12 12 12 12

4

ANALYSIS

147

in emphasis from Doom as a medieval monarch to him as a scientist, although Latveria itself remained near the top of both lists. The setting ‘Countryside’ also disappeared entirely from non-comics text, illustrating Doom’s presence within technological rather than natural settings. The United Nations Building appeared much more often in other media than in comics, something which was certainly borne out by the reading of the corpus as a whole. Here stories set at the United Nations seemed to take place all the time, often with Doom being voted in as leader of the world by UN representatives who had a lot more political power in the Marvel universe than in our own. New York also appeared more often, both in frequency and in rank, which was related to Doom’s use in these texts as a supporting character rather than the lead role he occasionally took in comics. In non-comics texts Doom was much more likely to enter the main storyworld where characters such as Spider-Man existed, acting as a protagonist and story generator rather than a lead character with his own settings. Doom travelled widely across the Marvel storyworld throughout ‘The Marvel Age’, but the settings he interacted with most often were those related to Latveria. There were differences that occurred across both media and time which related to the kind of stories Doom was part of, so he was more likely to interact with the Baxter Building during the Creation period but then moved into his own settings, including Latveria but also especially Battleworld by the Consolidation period. Similarly he was more likely to be shown in a technological setting in comics texts, but in each period and media type Latveria continued to feature prominently. There were 68 different settings logged, but of those Doom only interacted with 42. The fact that Doom did not interact with over a third of the settings that appeared in his texts demonstrates how often he was used as a supporting character. It also shows how large the Marvel Universe was, although it should be remembered that this was not a straightforward snapshot of this particular storyworld. The fact that it was based on appearances by Doctor Doom is a bias which informs the high probability of Latveria appearing. If the sample was based on Spider-Man, for example, one would expect the Daily Bugle to appear much more often, or Asgard for Thor. Indeed, this is evidence of how important settings are as a signifier of individual characters, who carry specific settings such as these around with them as part of their characters. They may exist in a ‘shared’ storyworld, but certain settings will always be shared less than others. It is clearly possible for Latveria to appear in a story without Doctor Doom, just as it is possible for the Baxter Building to appear without the Fantastic Four, but it is much more likely to appear in a story with the character who ‘owns’ it.

148

M. HIBBETT

Previous Events There were 59 texts which had references to previous events. Table 4.19 shows that the overwhelming majority of previous events were either brief recaps of the previous issue or ‘other’ one-off references to a previous issue which did not relate to Doctor Doom. Overall 71% of texts contained a reference to one or both of these. The large number of such ‘other’ references demonstrates, once again, that Doom was a wandering character often striding into the ongoing storylines of other characters. The most frequently cited past event that did directly relate to Doom was his original takeover of Latveria. This was never actually shown in the comics at the time, only referred to, and would not be shown in full until the Books of Doom mini-series [51] in 2005. Most of the other previous events referred either to aspects of his own origin or that of the Fantastic Four. The only notable events referred to with any frequency after that point were the murder Table 4.19 Previous events by texts Overall

Recap of previous issue Other Original takeover of Latveria Hitler/Nazi Germany/WW2 Various confrontations with FF Accident that destroyed his face Origin of Fantastic Four Secret Wars College days with Reed Richards First meeting with Fantastic Four Overthrow of Doom in FF #200 Previous attempts to cure The Thing Monks in Tibet Murder of Hauptmann’s brother Rama Tut/Kang history

Creation

Chaos

Consolidation

N

%

N

%

N

%

N

%

37 32 8

53.62 46.38 11.59

10 5 1

50 25 5

10 9 2

71.43 64.29 14.29

17 18 5

48.57 51.43 14.29

6

8.7

2

10

2

14.29

2

5.71

5

7.25

2

10

1

7.14

2

5.71

4

5.8

1

5

0

0

3

8.57

4

5.8

0

0

1

7.14

3

8.57

4 3

5.8 4.35

0 0

0 0

0 0

0 7.14

4 3

11.43 2.86

3

4.35

2

10

0

0

1

2.86

3

4.35

0

0

0

0

3

8.57

3

4.35

1

5

1

0

1

8.57

2 2

2.9 2.9

0 0

0 0

0 0

0 0

2 2

5.71 5.71

2

2.9

0

0

1

7.14

1

2.86

4

ANALYSIS

149

of Hauptmann in Fantastic Four #85 [5], Doom’s overthrow as leader of Latveria in Fantastic Four #200 [52], the events of Marvel Superheroes Secret Wars, and the ongoing confusion about whether Doom, Kang and Rama Tut were the same person. Apart from these the only other event that stands out was World War II, which was referred to several times during these stories, often comparing Doom or other characters to Hitler. As will be seen, this only occurred within comics texts, but did so regularly throughout the sample period. The number of previous events referred to per text rose between the Creation and Chaos periods and then fell slightly during Consolidation, as Fig. 4.14 shows. The reason for this was likely to be that there were simply less previous events to refer to during the initial Creation period, as the shared Marvel storyworld was in the early stages of its development. By the Chaos period the storyworld was not only established but was also being maintained and added to by a younger group of creators, many of whom came from fan backgrounds and enjoyed demonstrating their own knowledge of continuity by delving into, and giving references to, older stories [53]. In the Consolidation period, under the editorship of Jim Shooter, there was an attempt to curb these tendencies in order to make stories more accessible to new readers, hence the slight drop in numbers [54]. Figure 4.15 shows the percentage of texts in each period by number of referrals to previous events. In the Creation period almost all texts (90.71%) had either one or two references to previous events, while in the Chaos period there was a much greater spread. During this period only 42.71% of texts had 1–2 references, with the rest having more, mostly in the 3–4 references range. During the Consolidation period the frequency of references returned to something more like the Creation period, with a majority of texts (62.86%) in the 1–2 range 3.0 2.5 2.0 1.5 1.0 0.5 0.0 Creation

Chaos

Consolidation

Series 1

Fig. 4.14

Average number of previous events per text, by period

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60 50 40 30 20 10 0 Creation

Chaos 1

2

3

4

Consolidation 5

6

Fig. 4.15 Number of previous events per text by period

and other numbers falling off. This demonstrates again the effects of the arrival of fan creators in the Chaos period and then the moderate clampdown during Consolidation. The widely quoted article about comics by Umberto Eco suggested that comics exist in an oneiric climate, a dreamlike state ‘where what has happened before and what has happened after appear extremely hazy’ [55]. This may well have been true when the original article was written in 1962, but was certainly not the case when it was updated in 1972 to include modern characters such as the Fantastic Four, Spider-Man and ‘Devil’ (Eco’s misspelling of Daredevil) [37, 56]. Table 4.20 shows, however, that Eco may have had a point if he were discussing non-comics media which, in this sample at least, never included recaps of previous issues or episodes. Having said that, non-comics texts did still refer to previous events in general in a very similar way to comics texts, with the only major omissions being the later specific events such as Secret Wars, or those that only occurred in the comics, such as Doom’s overthrow from the Latverian throne and Hauptmann’s murder. They also did not mention World War II or Hitler, which is understandable as these would probably not be viewed as suitable issues to feature in cartoons designed for Saturday morning children’s television. These results show that Doom was deeply involved with the history of the Marvel storyworld, with references to previous events being made in the overwhelming majority of his stories. Compared to this overall use of continuity, however, previous events specific to Doom were included much less often and were almost always related to aspects of his origin, often as a means to introduce the character to new audiences when he appeared in a different series or other media.

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ANALYSIS

151

Table 4.20 Previous events by media type Previous events

Recap of previous issue Other Original takeover of Latveria Hitler/Nazi Germany/WW2 Various confrontations with FF Accident that destroyed his face Origin of FF Secret Wars College days with Reed Richards First meeting between FF and Doom Overthrow of Doom in FF #200 Previous attempts to cure The Thing Monks in Tibet Murder of Hauptmann’s brother in FF #85 Rama Tut/Kang history

Comics

Other

N

%

N

%

37 28 5 6 4 2 3 4 2 2 3 2 0 2 2

60.66 45.9 8.2 9.84 6.56 3.28 4.92 6.56 3.28 3.28 4.92 3.28 0 3.28 3.28

0 4 3 0 1 2 1 0 1 1 0 1 2 0 0

0 50 37.5 0 12.5 25 12.5 0 12.5 12.5 0 12.5 25 0 0

Aside from this there were very few previous events after his origin which were ever referred to again, and these all occurred within comics texts rather than in other media. This demonstrates that Doom was a ‘portable’ character, able to be dropped into the stories of other characters without too much explanation. This remained consistent across media and, for the most part, across time, although there was an increase in links across the storyworld during the Chaos period when, as discussed, former fans came on board as creators and were more inclined to celebrate the nature of a shared universe. The other conspicuous feature of this analysis was the high frequency of mentions of Hitler and World War II. Unlike, say, Captain America, Doctor Doom’s origin had no ties to this period and the nature of Marvel’s ‘sliding timescale’ (which continuously retrospectively compresses the storyworld’s timeline to stop characters ageing) meant that the single mention of Doom actually meeting Hitler would need to be either explained as time travel or omitted entirely in later years [57]. One explanation for the frequent reference to World War II could be that, for the creators involved, Hitler and the Nazis simply provided a reference point for dictators which Doom could be compared to, especially when he was acting as the hero of the story and was pitted against characters such as The Red Skull who were clearly more villainous than he was.

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Summary of Storyworld-Specific Components As with the analysis of character-specific components, analysis of these storyworld-specific components shows that Doom was largely stable across media but developed over time. His stability was particularly marked in the ‘other characters’ dimension, with almost all characters used appearing both in comics and other media. Some of the differences that were detected between comics and other media, such as the increased appearances by Spider-Man or New York, can be put down to the types of non-comics texts that were present during the time period of the sample, and the different story-requirements placed on Doom when he was appearing in them as a standard antagonist rather than the more complex protagonist that he became in comics during the Chaos period. The analysis also shows how much the Marvel Universe grew over the period of the sample, becoming a vast interconnected storyworld with hundreds of characters intermingling and interacting with settings and objects, and with a shared history. It may not have been a transmedia storyworld in the modern sense of a single narrative world telling one over-reaching coherent story across different media but the world which Doom carried with him, including other characters, settings, objects and history, was not only coherent but also developed across different media as time passed. As before, the implementation of this tool provides an empirical way to demonstrate these developments, particularly as they apply to the three different time periods identified. There was a clear change during the Chaos period, for instance, where the storyworld expanded and Doom roamed more widely across it. This was due to the new group of creators who entered Marvel’s employment during this period, and the different story opportunities offered by Doom’s appearances in Astonishing Tales and Super-Villain TeamUp which, in turn, were due to changes in the Comics Code and Marvel’s distribution deal. Later changes to the storyworld during the Consolidation period can be put down to the arrival of Jim Shooter as editor-in-chief and his attempts to enforce a simplified storytelling style on these creators.

Analysis of Behavioural Components This group of components is concerned with signifiers of Doctor Doom’s behaviour, covering the way he is perceived by others, his personality traits, and his motivations. Put together these can be seen as describing the way that the character-specific dimensions of the character interact with the wider storyworld-specific dimensions. Perceived Behaviour Overall there were 143 different words or phrases used to describe Doom across all texts, either by Doom himself, other characters or narratorial voices,

4

Table 4.21 Most popularly used descriptions of Doom

ANALYSIS

153

Description

Number of texts

Genius Evil Powerful Super-villain Dangerous Tyrant Menacing Demonic Creep Dictator Deadly Despot Monster Mad

16 14 13 11 10 8 7 7 7 6 6 5 5 5

with the majority (82 out of 143) only appearing once. Table 4.21 shows all the descriptions that appeared in five or more texts. None of the terms listed were particularly unexpected, but the sheer variety of words overall meant that it was difficult to come to any clear idea about how his behaviour was perceived within the texts. It might be, for example, that the word ‘genius’ was used more often than the word ‘evil’, but below that several other words were used that could be synonyms for ‘evil’. In order to solve this problem the results were recoded by grouping words together. This was done by starting with the most often used word—in this case ‘Genius’—then going through the list of all other words to identify any that were synonyms, using an online thesaurus where necessary. So here, for example, the words ‘Brilliant’ and ‘Intellectual’ were put into a group with ‘Genius’. This process was repeated for each un-grouped word, going through the entire list recoding wherever possible, with the results for all recoded words which appeared in two or more texts shown in Table 4.22. In all there were 45 separate recoded descriptions, with the 26 shown above appearing in more than two texts and another 19 which still only appeared once. This latter group included words and phrases like ‘leering’, ‘metallic’ and ‘a drag’. Of the words that appeared in at least 25% of texts (17 or more), three were clearly villainous—‘Evil’, ‘Dangerous’ and ‘Tyrant’—whereas the other four—‘Genius’, ‘Leader’, ‘Awesome’ and ‘Powerful’ were not, demonstrating that Doctor was not just a straightforward ‘baddy’. This mixture of descriptors was shown even more clearly by further recoding the words into six higher categories: Heroic, Villainous, Positive, Negative, Strong, Weak. For example, words in the ‘Good’ category were

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M. HIBBETT

Table 4.22 Recoded descriptions of Doom

Description

Number of texts

Evil Dangerous Genius Tyrant Leader Awesome Powerful Good Super-villain Ruthless Contemptuous Egomaniac Grotesque Mad Honourable Treacherous Creep Weak Cold Megalomaniac Majestic Fascinating Brooding Foolish Carefree Despicable

34 32 21 21 19 18 17 12 11 11 8 8 8 8 7 7 7 7 6 6 6 5 5 4 4 4

placed into ‘Heroic’, ‘Talented’ into ‘Positive’, ‘Foolish’ into ‘Weak’ and so on. When this further recoding was carried out the graph in Fig. 4.16 was produced, which shows that although there was a mixture of types of description, Doom was still described as ‘Villainous’ more often than he was ‘Heroic’, and ‘Negative’ descriptions were used more often than ‘Positive’ ones. However, the difference between the latter two was not particularly large, and Doom was also described as ‘Strong’ much more often than he was as ‘Weak’. Traditionally a super-villain represents the opposite of ‘the virtues and values of a society or culture’ which the superhero seeks to protect, and so one might expect such a villain to receive much more ‘Negative’ descriptions and almost no ‘Heroic’ ones [58]. Doctor Doom does not fit into this model, however, and is, in the words of Stan Lee, ‘far too complex to be neatly labelled as a typical bad guy’ [59]. Before assessing whether these descriptions differed over time or period it was first necessary to see whether the amount of underlying data changed over

4

ANALYSIS

155

45 40 35 30 25 20 15 10 5 0 Heroic

Villainous

Positive

Negative

Strong

Weak

Fig. 4.16 Further recoded descriptions

this time. Doing a straightforward count of how many times specific words were used per text would be meaningless without taking into account how many words there were overall, and how the frequency of descriptive terms per text changed over time. For instance, if it was the case that there were many more descriptive words used in texts during the Chaos period than Creation, there would be a larger pool of such words available in the former than the latter, and so some adjustment would be necessary before any conclusions about their use as descriptors could be drawn. For this reason the number of descriptions per text was counted by period and by media, and it was found that the average number of descriptive words applied to Doom varied very little over time, rising slightly from 4.45 words per text in the Creation period to 4.5 in Chaos and 4.77 in Consolidation. The range of different words per text did vary more widely, but this might simply be because of the larger number of texts sampled in Consolidation than Creation, and Creation than Chaos, as sampling more texts increased the chances of finding an outlier with more descriptions. Looking at comics versus other media types, there was a similar range of words per text, with a range of 0–27 for comics and 0–29 for other media. The average number of words was higher for comics (9.44) than other texts (8) but there did not seem to be any particular rationale for this—the other types of texts all mixed images with words so would, in theory, use descriptive terms as much as comics—and as the difference was so small no adjustment seemed necessary here either. With this pre-analysis completed, the next tables show the actual words used, firstly by period and then by media. Table 4.23 shows that there were some clear differences in the way Doom was described over time, with words such as ‘Awesome’ and ‘Dangerous’ falling away and ‘Genius’, ‘Leader’ and ‘Tyrant’ rising.

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M. HIBBETT

Table 4.23 Perceived behaviours by period Description

Awesome Brooding Carefree Cold Contemptuous Creep Dangerous Despicable Egomaniac Evil Fascinating Foolish Genius Good Grotesque Honourable Leader Mad Majestic Megalomaniac Powerful Ruthless Super-villain Treacherous Tyrant Weak

Creation

Chaos

Consolidation

N

%

N

%

N

%

9 1 0 1 1 2 14 3 2 15 1 2 9 3 2 1 1 2 0 1 4 3 4 0 3 1

45.00 5.00 0.00 5.00 5.00 10.00 70.00 15.00 10.00 75.00 5.00 10.00 45.00 15.00 10.00 5.00 5.00 10.00 0.00 5.00 20.00 15.00 20.00 0.00 15.00 5.00

3 1 1 1 1 0 6 1 1 8 0 2 2 3 0 1 6 1 2 2 4 1 6 2 4 0

21.43 7.14 7.14 7.14 7.14 0.00 42.86 7.14 7.14 57.14 0.00 14.29 14.29 21.43 0.00 7.14 42.86 7.14 14.29 14.29 28.57 7.14 42.86 14.29 28.57 0.00

6 3 3 4 6 5 12 0 5 11 4 0 10 6 6 5 12 5 4 3 9 7 1 5 14 6

17.14 8.57 8.57 11.43 17.14 14.29 34.29 0.00 14.29 31.43 11.43 0.00 28.57 17.14 17.14 14.29 34.29 14.29 11.43 8.57 25.71 20.00 2.86 14.29 40.00 17.14

In order to get a clearer idea of the overall change over time Fig. 4.17 shows these results with terms recoded as before. This shows that the use of broadly ‘Positive’ and ‘Negative’ words remained stable across periods, as did words describing Doom as ‘Weak’, while there were wider differences for words describing him as ‘Heroic’, ‘Villainous’ and ‘Strong’. These differences can be seen more clearly in Fig. 4.18, which shows that in each period Doom was much more likely to be described as ‘Villainous’ than any other type of word, but that the difference between this and ‘Heroic’ continued to shrink, and indeed that heroic words overtook ‘Strong’ words by the time the Consolidation period was reached. Further, he was more likely to be described with ‘Heroic’ words in the Chaos period than any other, but he was also more likely to be described with ‘Villainous’ words here too, indicating that it was this duality that was most interesting to creators at the time, with both sets of words appearing more

4

ANALYSIS

157

90 80 70 60 50 40 30 20 10 0 Heroic

Villainous

Positive

Creation

Negative

Chaos

Strong

Weak

Consolidation

Fig. 4.17 Recoded behaviours by period 90 80 70 60 50 40 30 20 10 0 Creation

Chaos Heroic

Villainous

Consolidation Strong

Fig. 4.18 Period by recoded behaviours

regularly. He was also more likely to be described as ‘Strong’ in this period too, a set of words which could be regarded as either heroic or villainous, depending on context. Once again, this gradual change over time demonstrates character development, and the fact that it occurred across different media is another clear argument for Doom being considered as an early transmedia character. Table 4.24 shows that a few individual words, such as ‘Egomaniac’ and ‘Evil’ were used fairly equally in comics and other media, but otherwise there was a lot of variation. Doom was more likely to be described in non-comics media as ‘Contemptuous’, ‘Genius’, ‘Fascinating’, ‘Grotesque’, Honourable’, ‘Mad’, ‘Powerful’, ‘Treacherous’, while in comics words such as ‘Good’, ‘Megalomaniac’ and ‘Weak’ were more common.

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M. HIBBETT

Table 4.24 Perceived behaviours by media Description

Awesome Brooding Carefree Cold Contemptuous Creep Dangerous Despicable Egomaniac Evil Fascinating Foolish Genius Good Grotesque Honourable Leader Mad Majestic Megalomaniac Powerful Ruthless Super-villain Treacherous Tyrant Weak

Comics

Non-comics

N

%

N

%

15 4 4 5 5 6 25 3 7 30 2 4 17 12 5 5 16 5 5 6 11 8 9 4 16 7

24.59 6.56 6.56 8.20 8.20 9.84 40.98 4.92 11.48 49.18 3.28 6.56 27.87 19.67 8.20 8.20 26.23 8.20 8.20 9.84 18.03 13.11 14.75 6.56 26.23 11.48

3 1 0 1 3 1 7 1 1 4 3 0 4 0 3 2 3 3 1 0 6 3 2 3 5 0

37.50 12.50 0.00 12.50 37.50 12.50 87.50 12.50 12.50 50.00 37.50 0.00 50.00 0.00 37.50 25.00 37.50 37.50 12.50 0.00 75.00 37.50 25.00 37.50 62.50 0.00

There did not seem to be any particular pattern to these differences, so perhaps they were simply due to different vocabularies in different media. With this in mind Fig. 4.19 shows the words grouped into broader categories again. Recoding in this way clarifies matters significantly, and shows that, for many behavioural aspects, the comics and other media described Doom in much the same way despite using different individual words. He was described as ‘Heroic’, ‘Positive’, ‘Negative’ and ‘Weak’ in roughly equal amounts in both. The description ‘Villainous’ was the most used in all media, although he was more likely to be described as such in other media than in comics. This might be because of the need to clearly establish his position in these types of story. Audiences for non-comics media would not be expected to have such in-depth knowledge of the character relationships, and so these descriptions may have been emphasised in order to make this clear.

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ANALYSIS

159

80 70 60 50 40 30 20 10 0 Heroic

Villainous

Positive Comics

Negative

Strong

Weak

Other

Fig. 4.19 Recoded behaviours by media

The only major difference between comics and other media was in the use of ‘Strong’ descriptive words, which were more likely to be used than any group apart from ‘Villainous’. It is possible that these words might also have been emphasised in a similar way to ‘Villainous’, to demonstrate that Doom was a formidable enemy, although it was notable that ‘Negative’ descriptive words were not also used more often. This shows that although Doom’s villainy needed to be accentuated for an audience who would not be expected to be familiar with him, he remained a complex individual who could still be seen in a favourable, even sympathetic light, irrespective of where he appeared. Thus, Doom’s appeal to an audience as someone whose behaviour could at least be understood, if not necessarily agreed with, is an essential transmedia signifier of the character. Overall, descriptions of Doom remain fairly stable over time, with him more often described as ‘Villainous’ than ‘Heroic’, ‘Strong’ more than ‘Weak’ and ‘Negative’ descriptions used slightly more often than ‘Positive’ ones. This mixture of descriptions once again shows that he was not a traditional, all-evil all-the-time, super-villain, but rather a nuanced character that audiences might be expected to sympathise with more than a straightforwardly evil character such as The Red Skull. Within time periods, however, character evolution can be seen during the Chaos period as textual authors grappled with the nature of the character, using different descriptions to discuss whether his ‘strong’ actions made him a hero or villain. Across media the descriptions remained stable although, as discussed, he was more likely to be described as ‘Villainous’ and ‘Strong’ in other media than in comics. This may have been because of the need to clearly position his role within the narratives. Whether it indicates a change in his actual behaviour will be seen in the next section.

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Behaviour—BFI This section deals with data derived from the use of the BFI-10 questionnaire, whereby questions were answered on aspects of Doom’s overall personality for each text. While entering the data for this dimension my personal impression was that it was extremely varied and unreliable. Unlike in other dimensions there did not seem to be any pattern emerging, and I was convinced that the analysis would show that it was not useful at all. However, when I came to actually do the analysis I found quite the opposite. Once the ten questions had been answered for each text they were combined into scores for Extraversion, Agreeableness, Conscientiousness, Neuroticism and Openness to Experience. As discussed previously, each of these scores combined responses from two questions ranked 1–5 (reversed where necessary), and so the final scores were in the range 2–10, with a lower score representing a lower rating for each section, e.g. a score of 2 for Agreeableness showed that the character was not very agreeable at all. In line with advice received from a statistical advisor, only very basic statistical analyses were carried out on the data that was thus gathered.1 Firstly, a median for each score was calculated. This is a measure of central tendency which locates the midpoint of a dataset [60]. It can be used instead of a ‘mean’ average for categorical or ordinal (ranked) data such as that represented by these scores [61]. Calculating the medians for each score produced the results shown in the first column of Table 4.25. These median values show that Doom scored lowest on ‘Agreeableness’, was in the mid-range for ‘Neuroticism’, and scored highly for ‘Extraversion’, ‘Conscientiousness’ and ‘Openness to experience’. This tallied remarkably well with the Doctor Doom that was subjectively witnessed during close reading of the corpus overall. He was definitely not very agreeable, but was always ready to express his own greatness, extremely conscientious when it came to his cunning plans, and was constantly on the lookout for new ways to conquer the world. Table 4.25 Overall results for Doom’s big five personality traits

Trait

Median

Range

MAD

Extraversion Agreeableness Conscientiousness Neuroticism Openness to Experience

7.50 3.00 8.00 5.50 7.00

4–10 2–8 3–10 2–10 6–10

1.40 1.50 1.26 2.26 0.88

1 Discussion via Microsoft Teams with Paul Lambert, Professor of Biostatistics at University of Leicester, on 9 September 2020.

4

ANALYSIS

161

What was not clear from this was how much the results varied across the whole dataset. One way to find out would be to look at the dispersion of data—that is, the range of scores from lowest to highest values in each section. The range column in Table 4.25 shows that Doom did not score at either extreme for Agreeableness, scored generally highly for Openness To Experience, and then had a wide range of scores for Extraversion, Conscientiousness and Neuroticism. However, this only tells us that there was at least one score at either end—it could be, for instance, that Doom only scored 3 and 10 once each for Conscientiousness, with most of his other scores being 8. A much more useful way to look at this was to analyse the deviation of the data—how much it varied from the chosen measure of central tendency [62]. This would show whether Doom’s scores were generally stable for each score, which would demonstrate coherence, or varied widely, indicating the opposite. The method chosen to calculate this deviation was the Mean Absolute Deviation around the median—how much, on average, the scores deviated from the median result. Using this method had three main benefits: 1. It has been proven to be a robust measure of deviance for the analysis of single pieces of ordinal data [63]. 2. It is much simpler to calculate than other measures, such as standard deviation [64]. 3. The standard statistical acronym for this method is MAD, which sounds like the sort of statistics Doom would use himself.2 The MAD is obtained by first calculating the overall median of scores, then for each individual score calculating the absolute difference from the median. ‘Absolute’ here means irrespective of whether the calculated result was positive or negative, so for example if the median was 5 then scores of 3 and 7 would both have an absolute difference of 2. Once this was calculated for all texts the final MAD was calculated from the mean average of deviations, i.e. by adding all of the absolute deviations together and dividing by the number of texts. This is shown in the final column of Table 4.25. The MAD for each score therefore indicates how much, on average, Doom’s scores varied from the midpoint, hence a higher value showed higher variance and vice versa. For example, the above table shows that Doom’s scores varied most in the trait ‘Neuroticism’ and least in ‘Openness to Experience’. This can be further explored by examining the results for each personality trait in more detail. For example, Fig. 4.20 shows that Doom’s openness to experience scored consistently highly, with a range from 5 to 9 and a very small MAD of 0.88. This low MAD shows that the score did not vary much,

2

He might initially choose to call it ‘Doomstats’, of course, but then this would be too similar to ‘Doomstadt’, the capital of Latveria, and the confusion between the two would be likely to lead to a lot of statisticians meeting untimely ends.

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M. HIBBETT

something which can be seen clearly in the graph from the grouping together of scores. This openness to experience was demonstrated by the number of plots which revolved around Doom’s need to find a new source of power by which he might gain mastery of the world. This was the case whether he was the lead character or guest villain, and as shown here was consistent throughout the sample. Figure 4.21 shows that Doom scored highly for conscientiousness, with a median score of 8 The MAD was only 1.26, which indicates that this characteristic was stable, although as can be seen there was a much wider range of scores than for openness to experience, showing that he did act out of character sometimes. The 18 16 Median MAD

14 Texts

12 10 8 6 4 2 0 2

Fig. 4.20

3

4

5

6 Score

7

8

9

10

5

6 Score

7

8

9

10

Openness to experience 12 10

Median MAD

Texts

8 6 4 2 0 2

Fig. 4.21

3

Conscientiousness

4

4

ANALYSIS

163

lowest score here came from Not Brand Echh #12, a series in which Doom did not generally act in character anyway, but the others come from appearances in series such as Daredevil and Iron Man where one might expect him to have been portrayed more consistently. The shape of the graph, with the median score in the centre and other scores dropping gradually either side, is roughly that of a bell curve [60]. This, in very broad terms, is a graph produced when a sample has a ‘normal distribution’, which means that we can generally expect that scores are more likely to occur around the median [61]. As stated earlier, there are not enough cases in the sample to provide a statistically significant analysis of this data, but the shape of this graph does at least hint that this aspect of Doom’s behaviour was very consistent. The graph for extraversion in Fig. 4.22 is not quite so pleasingly bellshaped, although it does peak at the score of 8, which is close to the median of 7.5, and other scores do generally decline as they move away from it, with most contained within the relatively narrow MAD of 1.40, demonstrating that his character was fairly stable in this dimension. Figure 4.23 shows that Doom generally scored very low for agreeableness, with an MAD of 1.5 showing again that this is a consistent, and not unexpected, trait. Doom did score outside of the MAD range on several occasions here, although the general trend of the data is that he was much more likely to have a low score for this dimension. What higher scores there were here occurred mostly in texts such as Super-villain Team-Up where he was the lead character and thus might be expected to be portrayed more sympathetically. Out of all the categories of personality trait, neuroticism was the one that varied the most. As Fig. 4.24 shows, there was no real consistency at all here, 12 10

Median MAD

Texts

8 6 4 2 0 2

Fig. 4.22

Extraversion

3

4

5

6 Score

7

8

9

10

164

M. HIBBETT

16 14 Median MAD

Texts

12 10 8 6 4 2 0 2

Fig. 4.23

3

4

5

6 Score

7

8

9

10

Agreeableness

with scores for neuroticism/emotional stability ranging from the lowest to highest possible, with a wide MAD and the median score in the centre. Some of this can be explained by the fact that the highest scores of 9 and 10 all came from series that featured alternate versions of Doom—Not Brand Echh, Spidey Super Stories and What If ?—where the version of Doom might be expected to differ from the standard portrayal. In What If ? the whole point of the series was to examine storylines where events moved differently, such as What If ? #22 ‘What if Doctor Doom has become a hero?’ [65]. Similarly Doom was often shown acting out of character for comic effect in Not Brand Echh, while Spidey Super Stories featured simplified stories aimed at younger children, where some characteristics were exaggerated to make them easier to 12 10

Median MAD

Texts

8 6 4 2 0 2

Fig. 4.24

Neuroticism

3

4

5

6 Score

7

8

9

10

4

ANALYSIS

165

understand. However, if this were the full explanation then one would expect similar differences to occur across all dimensions, which they did not. There was occasional consistency within series, with Doom tending to score lower in Super-Villain Team-up for example, but then having scores ranging from 2 to 7 in different issues of The Fantastic Four. It was also noticeable that Doom tended to score lower (indicating more emotional stability) when he was the protagonist in Super-Villain Team-Up and Secret Wars than elsewhere where he was used as antagonist, showing that this category was more dependent on the needs of plot than on adherence to the character’s fictional personality. Another explanation for this changeability could be that Doom behaved differently according to time period and media type. Figure 4.25 shows data on neuroticism split into comics and other media, and demonstrates that the unusual variation for neuroticism came mostly from comics texts. There was not a great deal of available data about ‘Other media’, but nonetheless it shows that Doom was relatively consistent here, scoring within the MAD for all except one text, the Spider-Man cartoon series [27]. For comics, however, two distinct groups of data were present, with 19 texts scoring 2 or 3, and all others scoring at least 5. The gap between these two groups indicates that there were two different versions of Doom in action, at least for this dimension. In order to investigate this further, Fig. 4.26 looks just at comics, divided into the three time periods of Creation, Chaos and Consolidation. These show that Doom’s scores were very consistent during the Creation period, especially taking into consideration the fact that the scores of 9 and 10 both came from issues of Not Brand Echh where he would not necessarily be expected to behave consistently. If these were removed then the MAD would be much smaller. Doom’s behaviour in this dimension changed dramatically during the Chaos period, with the median moving significantly towards the lower end of the scale and a wider MAD, indicating more changeability. The highest score of 8 came from an issue of Spidey Super Stories where, again, Doom might Other media

Comics 12

12

10

10

Median MAD

Median MAD

8 Texts

Texts

8 6

6

4

4

2

2

0

0 2

Fig. 4.25

3

4

5

6 Score

7

8

9

Neuroticism by media type

10

2

3

4

5

6 Score

7

8

9

10

166

M. HIBBETT Creation

Chaos

8

8

7

7 Median MAD

Median MAD

6

5

Texts

Texts

6

4

5 4

3

3

2

2

1

1

0

0 2

3

4

5

6 Score

7

8

9

10

2

3

4

5

6 Score

7

8

9

10

Consolidation 8 7 Median MAD

Texts

6 5 4 3 2 1 0 2

Fig. 4.26

3

4

5

6 Score

7

8

9

10

Neuroticism for comics in each period

be expected to behave slightly differently, and if this was removed the change would again be even more marked, with a much reduced MAD indicating that in this period Doom became much more controlled. This is likely due to the fact that he was being used as a protagonist in series such as Astonishing Tales and Super-villain Team-up, and reflects the results for this period found in the perceived behaviours dimension. It is thus in the final Consolidation period that his characteristics split into the two distinct groups mentioned earlier, showing that there were two different versions of the character in play. Examining the data for the individual texts in this period reveals no recognisable pattern. The two highest scores came from series which existed outside the mainstream Marvel storyworld. Spidey Super Stories #53 [13] scored the maximum 10 for neuroticism and What If ? #18 [66] scored 9, but scores for comics set in the core storyworld also varied widely. For example, Doom scored 2 in Fantastic Four #246 [67], 7 in Fantastic Four #198 [34] and 2 in Marvel Super-Heroes Secret Wars #6 [68] but 8 in Marvel Super-Heroes Secret Wars #7 [38] a month later. Ironically, Doom’s emotional stability was varying wildly during a period when creators were supposedly being dictated to more than ever before about plots and character behaviours. Even more ironically, the main culprit of this was the writer of Secret Wars, Jim Shooter, who was also the person attempting to enforce these diktats on others. This may be due to the way that this series was constructed, with the toymakers Mattel exerting influence on Marvel to showcase certain characters in

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certain ways. However, having read the entire series as part of the close reading for the Marvel Age Doom blog, this does not entirely explain the problem, as the overall plot often seemed to be being made up on the spot and consisted of an enormous amount of earnest talking which one would not expect to be requested by a toy-maker. The series also features a graphic murder, a sub-plot involving a character being unfaithful to his under-age girlfriend, a suburban sitcom about a cosmic being falling in love with a supposedly overweight super-villain, a lot of talking about the weather, and at least one roll call of all the characters in every issue. Part of the point of an empirical study is to avoid value judgements on story content, but it can be said that the lack of consistency in Doom’s character in this series is at least partly due to the overall tone of the series itself. Overall, these results show that using the BFI-10 is an effective way of describing character coherence for personality traits over time, offering empirical methods to not only show what a character’s personality traits actually are, but also how they cohere or change over time. Here the methodology has demonstrated that Doom was consistently disagreeable, extroverted, conscientious and open to new experiences. It also highlights the lack of consistency in neuroticism/emotional stability, with further analysis showing which time periods and media types these changes occurred in. Here, neuroticism/emotional stability can be seen to have changed from neither stable nor unstable during Creation to very stable during Chaos, but then to vary widely throughout the Consolidation period. However, this was only the case in comics texts, with non-comics texts maintaining stability. As has been noted for other dimensions, all of this information might be uncovered using non-empirical methods, such as a close reading of the texts, but it would not be so easy to drill down into the data to find possible explanations, and indeed it might be missed altogether. Motivations There were 56 texts where motivations for Doctor Doom were expressed, with the remaining 13 texts generally featuring brief cameo appearances. There were 33 different motivations recorded altogether which were grouped into smaller categories using a similar method to that used to recode perceived behaviours. The results are shown in Table 4.26. These results show that Doom was heavily driven by a need to prove himself, primarily in terms of his general superiority over other people, but also by a need to have his honour demonstrated, to receive general acclaim from other people, or to be revenged on those who had wronged him. He was also motivated by the quest for power, whether global, local to Latveria, or in terms of the accumulation of powerful items. Hatred of superheroes in general was an important motivator, but contrary to the results of

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Table 4.26 Recoded motivations across all texts

Motivation

N

%

Prove his superiority Rule the world Acclaim/approval of others General hatred of superheroes Rule of Latveria Self-preservation Revenge Intellectual curiosity Relief from boredom Friendship Sexual attraction Profit Physical vanity Power Make people unhappy

27 24 20 15 15 14 13 13 7 6 5 5 5 4 4

48.21 42.86 35.71 26.79 26.79 25.00 23.21 23.21 12.50 10.71 8.93 8.93 8.93 7.14 7.14

the fan survey, his hatred of the Fantastic Four came fairly low down in this list, with his specific hatred of Reed Richards only appearing in three texts. Other important motivations were self-preservation and activities of the mind, whether that was intellectual curiosity or a general relief from the boredom of the status quo. What makes this dimension different from many of the others is that there were no signifiers that appeared in the more than half of all texts. Instead Doom had a wide range of motivations which, as shown in Fig. 4.27, changed over time. 70 60 50 40 30 20 10 0

Creation

Chaos

Fig. 4.27 Recoded motivations by period

Consolidation

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His desire to prove his superiority over others, for example, gradually declined, as did his need for revenge, while his hatred for superheroes and concern for Latveria rose. During the Chaos period he was much more driven by a need to rule the world and less so by intellectual curiosity, relief from boredom or by a desire for acclaim from others. This change occurred during a time when Doom was used as a protagonist as well as antagonist, so one might expect that his motivations would become more like those of a hero, rather than become even more like a world-dominating super-villain, but these changes are in line with those in other dimensions during this period, and possible reasons for this will be discussed shortly. Figure 4.28 shows that Doom was much more likely to be motivated by a desire to rule the world in non-comics media than he was in comics. Similarly his hatred of superheroes and desire for self-preservation and revenge were more prevalent in non-comics texts, where there was also a complete absence of friendship as a motivation. Although the dataset for other media is very small, these observations are borne out by the qualitative examination of the full sample which took place over several years for the Marvel Age Doom process blog [69]. These analyses show that Doom was a complicated character motivationally, with many different impulses driving his behaviour. No single motivation took priority overall, although many of them could be summarised under a general need to prove his worth and superiority, and to maintain control over his environment. In comics, this mixture of motivations developed over time, and this evolution was in line with the similar development in the way that Doom grew from a standard super-villain into a more nuanced character who increasingly took a leading role in stories. 100 90 80 70 60 50 40 30 20 10 0

Comics

Other

Fig. 4.28 Recoded motivations by media type

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However, in other media Doom was less nuanced, driven by more traditional super-villain motivations such as power, hatred and revenge. As stated previously, this may be just because he was employed in these other media as a villainous antagonist for the main protagonist to fight against, and so his motivations needed to be different. This was in line with other behavioural dimensions, where Doom also acted slightly differently in non-comics media, but was dissimilar to the character-specific and storyworld-specific dimensions where, broadly speaking, changes to Doom and his world happened across media as a development of his character. Summary of Behavioural Components Throughout the sample Doom was seen as a strong, villainous character. Despite this view of him as a villain, however, he was also often described as heroic while negative perceptions only slightly outweighed the positive. This was one of the attractions of Doom as a guest character, in that his complexities allowed for many different stories to be told, and it is noticeable that he was more motivationally complex in comics than non-comics, with the latter more often using him as a straightforward villain. He was similarly stable in terms of personality traits, consistently displaying high levels of openness to experience , and conscientiousness, low levels of agreeableness, and moderate extroversion. This was the same across media, apart from neuroticism. In this dimension he was stable in non-comics media as moderately neurotic, but was highly variable in comics texts during the Chaos and Consolidation periods. Doom’s motivations, although varied, can be grouped together as a need for power and to prove himself as better than everybody else, and these remained stable across time and media, although he did evolve over time, becoming more focused on world-domination during the Chaos period. This is slightly surprising, as this was also a time during which he was being used more often as a heroic character, or at least a protagonist, so one might expect his motivations to have been more altruistic. However, this counter-intuitive pairing might offer an answer to the change in behavioural dimensions overall. ‘The Marvel Age’ began during the Cold War, when America faced a clear enemy in the Soviet Union, and any nation that stood against the Russia could be considered an ally [70] In this way, during the Creation period, Doom could be seen as a beloved national leader of a non-Soviet East European nation who sought power as a way to protect his people and thus, like similar real-world figures such as General Tito in Yugoslavia, could be seen as at least partly laudable [71]. However, towards the end of the 1960s domestic attitudes towards American foreign policy began to change, with ongoing disquiet over Vietnam and a few years later the Watergate scandal meaning that the ‘moral certainty of the consensus of the 1960s became the existential ambiguity of the 1970s’ [72]. This coincided with the Chaos period, when a new, younger, group of

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creators began working at Marvel who would be likely to have a more cynical political attitude than the original Marvel ‘bullpen’, many of whom fought in World War II. These new creators, and this change in political outlook, meant that Doom could be used to comment on current events. As Fig. 4.29 shows, this was sometimes done rather bluntly. During this period Doom was seen as an oppressive dictator, scornful of his subjects, who can thus be situated as a lead character without necessarily being approved of, in the mould of similar anti-heroes of this time like the unapologetically fascist Judge Dredd in 2000AD, launched in 1977 [74]. Doom’s reversion to a slightly less villainous role during the Consolidation period can similarly be linked to both changes in the organisational structure at Marvel and general political attitudes. Jim Shooter installation as editor-inchief led to the departure of many of the writers who had joined Marvel during the Chaos period, and this roughly coincided with Ronald Reagan’s election as president. Under Reagan, America became more interventionist in foreign affairs, often working covertly to directly support dictators like Ferdinand Marcos or General Pinochet who were seen as taking a strong stand against communism [75]. Doom explicitly situated himself as one such dictator in Fantastic Four #247, stating: When I seized control of my native land she was a pitiful joke of a country, with a feudal monarchy that was slowly destroying her. Within months Latveria would have been swallowed by the communist lands which surround our hills [76].

Fig. 4.29 Doom joins forces with Henry Kissinger in Super-Villain Team-Up #7 [73]

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Once again he was a complex, corrupt, confusing presence, but this time he was also a vital ally to the pure, honourable Americans in their struggle against the enemy which Ronald Reagan described as ‘the evil empire’ [77]. There is more—much, much more—to be said about Doctor Doom as a reflection of changes in US foreign policy, but for the purposes of this project it is enough to say that the change in his personality which occurred during the Chaos period, in comics at least, can be considered as at least partly due to changes in US political attitudes over the course of ‘The Marvel Age’. It is also worth noting here that these changes in behaviour were highlighted as a result of examining his behaviour as three separate categories within their own group of transmedia character components, rather than as a single aspect of character-specific components, demonstrating the worth of arranging the catalogue in this way.

Analysis of Authorship Components This final group concerns the people, or organisations in our world, rather than a fictional storyworld, who can be seen as the authors of these texts. Market Authors There were 21 different Market Authors identified altogether. Most of these only appeared once in the dataset, and so the results were recoded into the broader groups shown in Table 4.27. The only text in the sample that did not have Marvel Comics as a Market Author was the Power Records release. It did have ‘© Marvel Comics Group’ in tiny writing on the front, but that was barely legible and seemed to be a legal requirement rather than market authorship. Apart from that, some form of the brand name ‘Marvel’ was always used to denote market authorship of Doctor Doom, regardless of period or media type. Table 4.27 Recoded market authors Market author

Marvel CC/IND (distributors) Stan Lee Presents Other company Other creator

Overall

Creation

Chaos

Consolidation

N

%

N

%

N

%

N

%

68 50

98.55 72.46

20 17

100.00 85.00

13 12

92.86 85.71

35 31

100.00 88.57

37

53.62

0

0.00

11

78.57

26

74.29

7

10.14

2

10.00

2

14.29

3

8.57

2

2.90

0

0.00

0

0.00

2

5.71

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The various distribution companies also appeared regularly on the cover, although as with the Power Records example mentioned above, this was also displayed in small text for legal reasons on comics texts only, as shown in Fig. 4.30, rather than as an attempt to sell the text itself. Stan Lee was the only individual regularly named as a Market Author, in the form of the ‘Stan Lee Presents’ introductory box which was used in almost half of all texts although, as shown later, this appeared only in comics. ‘Stan Lee Presents’ did not appear until the Chaos period, a period partly defined by his stepping down as editor and writer, at which point he relinquished his role as a textual author, in comics if not entirely in other media, and became a Market Author instead. The decline in the importance of distribution companies during the Consolidation period, due to the arrival of the Direct Market, can be seen here as well, as Marvel began to distribute their products directly to comics stores, rather than needing other companies [53, 79].

Fig. 4.30 Logo for Curtis circulation (‘CC’) in top left corner of The Invincible Iron Man #74 [78]

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Table 4.28 Recoded market authors by media type Market author

Marvel CC/IND (distributors) Stan Lee Other company Other creator

Comics

Other media

N

%

N

%

61 50 37 2 2

100.00 81.97 60.66 3.28 3.28

7 0 0 5 0

87.5 0 0 62.5 0

However, Table 4.28 shows that although Marvel’s name dominated throughout, the way it was presented in other media varied. It was referred to as ‘Marvel Productions Limited’, ‘Marvel Comics’ and ‘Marvel Comics Magazine’, ‘Marvel Comics Group’ in newspapers or just ‘Marvel’ elsewhere. During the Creation period these variations might have been due to Martin Goodman’s propensity for giving his companies different names, but this continued through all periods [53, 80]. The name ‘Marvel’ was used in almost all non-comics texts as well, but other signifiers found in comics texts were not. Non-comics texts were not distributed by comics distribution companies, so they would not be expected to appear, but the names of creators, especially Stan Lee, as Market Authors are notable by their absence. Instead, other companies apart from Marvel were named in almost two thirds of these texts. This was due to the fact that Marvel at this time did not have the capacity or ability to produce non-comics texts, and so went into partnership with other companies who did. Thus Power Records, Krantz Films, Grantray-Lawrence Animation, Hanna-Barbera Productions, TSR Inc and Register and Tribune Syndicate were all named, although none more than twice in the sample. An explanation for this could be that the non-comics production companies had little knowledge or interest in the textual authors who produced their source material, or of the fan community who would recognise them, and so did not see any worth in using these names to market their own products. It might also be that giving prominent recognition to creators could lead to demands for higher, or indeed any, royalty payments. This practice of not using the original creators as Market Authors in other media has continued into the twenty-first century, with such creators generally being credited as textual authors in the end titles of Marvel movies but rarely named in marketing materials, even when their work forms the basis of the film. The only major exception to this is Stan Lee himself, whose cameos in almost every Marvel film made him not only a recurring character within it, as he was in the comics, but also a signifier that this was a valid Marvel text.

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In all media, across all time periods, Doom was thus defined as a Marvel character. Other Market Authors did appear in the sample, although these were either distribution companies who were listed in much smaller text sizes than the ‘Marvel’ branding, or isolated examples of partner companies on noncomics media. The only Market Author that rivalled Marvel in any way was Stan Lee, whose name and image would be used almost as a synonym for ‘Marvel’ from the point at which he ceased being a textual author of comics, and then on for most of the rest of his life. Textual Authors The names of textual authors were taken from the texts themselves. In most cases there would have been many individuals involved in the creation of texts who were not credited in this way. This was common practice in the comics industry at the time, and included authors moonlighting from other companies, assistants in the studios of more well-known artists, or the many other workers who contributed to the production of these stories like early colourists, printers, distribution managers and so on [81, 82]. However, this analysis concentrates on those who were directly credited within the texts as having the authorship of Doom’s stories as it was these names that would be seen by consumers of the texts as signifiers. Overall there were 164 people whose names were associated with Doom’s stories. Table 4.29 shows those whose names appeared five or more times. Table 4.29 Textual authors with 5 or more texts

Textual author

Texts

Jim Shooter Stan Lee Joe Rosen Jack Kirby Artie Simek Joe Sinnott Bill Mantlo Jim Salicrup Tom Orzechowski Glynis Wein Sam Rosen Roy Thomas Mike Esposito Tom DeFalco Christie Scheele Archie Goodwin John Byrne Jim Novak

29 23 15 10 10 10 8 7 7 7 6 6 6 6 5 5 5 5

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Jim Shooter appeared most often because, as well as writing or pencilling some stories, he was credited as ‘editor-in-chief’ on almost all texts created during the Consolidation period. Similarly, Stan Lee’s name appeared as writer, editor or both on almost all texts produced during the Creation period, none at all during Chaos, and then four non-comics texts during Consolidation— almost the reverse pattern to his appearances over time as a Market Author. Among the other main textual authors there were many who would not necessarily be the writers and pencillers that fans would most associate with the stories. Almost all of the textual authors mentioned in the fan survey were writers and artists, but the vast majority here were not. The only creators in the above list who were solely credited as writers or artists, with no credited editorial responsibility, were Jack Kirby, Bill Mantlo and John Byrne. Joe Rosen, Artie Simek, Tom Orzechowski, Sam Rosen and Jim Novak were all letterers, while Glynis Wein and Christie Scheele were colourists. These creators would work on many more titles per month than writers, pencillers and inkers and so would be expected to appear more often. Indeed, colourists especially would have appeared more often in the sample but for the fact that they were not regularly credited until the Chaos period. The majority of textual authors (97 out of 164) only appeared once in the sample. For the remaining 67 textual authors, 61 did their work on more than one series, demonstrating that Doom did not have any consistency of creative team. The only textual authors whose work on Doctor Doom was confined to a single series were John Beatty (3 issues of Marvel Super-Heroes Secret Wars ), Marie Severin (3 issues of Not Brand Echh), Pablo Marcos (2 issues of The Avengers ), Peter Gillis (2 issues of What If ?), Wally Wood (2 issues of Astonishing Tales ) and Win Mortimer (2 issues of Spidey Super Stories ). In almost all cases, textual authors stuck to a single media type. The only textual authors to move across media types were Stan Lee (cartoons, comics and newspaper strip), Jack Kirby (cartoons and comics), Gene Colan (cartoons and comics), Larry Lieber (newspaper strip and comics), Mike Zeck (comics and TSR roleplaying game) and Bob Layton (comics and TSR roleplaying game). Nor was there much movement of textual authors across periods, with only 31 textual authors (19%) working on Doom texts across more than one period of ‘The Marvel Age’, and only four (Joe Sinnott, Larry Lieber, Roy Thomas and Mike Esposito) involved across all three. Doom very rarely appeared for extended runs by the same creators—indeed, even when he appeared in his own series, such as Astonishing Tales or SuperVillain Team-Up, the creative teams changed every few issues—and so one might expect his other character components to vary. With a character like Spider-Man, for example, there would be specific editorial staff whose job it was to oversee continuity for the character, ensuring that his costume, supporting cast, personality and so on was consistent from one month to the next. Without this it would be very easy for a character like Doom to change

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from appearance to appearance, and so the fact that he does not do so is remarkable. Overall this analysis shows that Doom’s textual authors varied across time, media and individual series, meaning that, for this category, he was not coherent at all. Rather, Doom was used as a portable character ‘owned’ by no single individual, but instead re-used by many as a useful story generator. Summary of Authorship Components Doctor Doom was extremely consistent in terms of market authorship, and extremely inconsistent for textual authorship. Throughout the sample he was almost always marketed as belonging to Marvel, regardless of what sort of media he appeared in. Other market authors appeared in comics and non-comics media, but there was no crossover between the two, with no person or organisation named as a market author for comics appearing as the same for non-comics, and vice versa. This may in part explain why, in the signifier survey, ‘Marvel’ was the fifth most-named textual author as well as the most-named market author, as the company name became so associated with the character. This may also be due to the fact that Doom did not really have specific textual authors of his own during ‘The Marvel Age’. In the survey Stan Lee and Jack Kirby were named far more times than any other authors, and it is true that they did work on a larger number of Doom texts than most others. However, these texts were spaced apart, with Doom rarely appearing in more than two or three consecutive issues of a series. Even when he did briefly appear in series where he was the lead character, these titles did not maintain the same creative teams for very long.

Conclusion In this chapter the unified catalogue of transmedia character components has been used as a way to analyse Doctor Doom as a character during ‘The Marvel Age’ in terms of character, storyworld, behaviour and authorship. It has shown that his character-specific components were largely stable, developing over time but doing in so in a way that was coherent across different media. His storyworld also developed in a similar way, with a clear growth in its complexity during the Chaos period when the wider ‘Marvel Universe’ expanded. Doom’s behaviour was similar in some ways, with his personality traits and the way he was described developing over time, becoming generally more heroic. However, the BFI showed some inconsistency, notably in terms of the ‘neuroticism/emotional stability’ category and especially in comics texts. Doom’s market authorship was one of the most stable aspects of all, with ‘Marvel’ being named as a market author in almost all cases. However his textual authorship was coherent only in the fact that it changed constantly

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over time and media as different creators used him in their stories. With this in mind it is perhaps surprising to see that Doom’s coherence in most other dimensions is so stable. As stated, for most of ‘The Marvel Age’ he was picked up and used across a wide range of titles and series by many different authors, and so we might expect continuity errors to creep in, eroding his coherence. The fact that this does not happen is, I would argue, due to the fact that once his core characteristics were established early on in the Creation period they did not need to be changed for him to be effective in whatever text he was placed in. To put it another way, the reason that Doom was used so often by multiple creators is that he was such a useful creation, with a clear set of signifiers which were easily understood, and which could power stories again and again without amendment. This might also explain why Doom’s other signifiers did not change much over time, as they simply did not need to. He was sufficiently complicated to generate plots without being tinkered with, and so he was able to maintain coherence in almost all dimensions apart from textual authorship because the signifiers in those other dimensions worked so well together. As Stan Lee said, ‘Sometimes you hit a homer first time at the bat. That’s how it was with Jack and me—and Doctor Doom’ [83]. The data analysis has demonstrated that the thirteen-dimensional unified catalogue of transmedia character components was a highly effective tool for examining the character coherence of Doctor Doom over time and media. It provided empirical evidence for developments which might otherwise have been based on conjecture, while also uncovering character signifiers that would not necessarily be revealed using more conventional humanities-based analyses. This will be examined more fully in the final ‘Discussion’ chapter. However, before then it is worth asking whether the tool can actually be used for examining characters other than Doctor Doom. Many of the existing models of transmedia character components were developed to codify specific characters—for example, Uricchio and Pearson’s analysis of Batman— and by concentrating on the requirements of these particular analyses these models missed out factors which would have been useful elsewhere. The same argument could well be made here, that the unified catalogue of transmedia character components has been set up to analyse Doctor Doom over time and media, so might be useless for other characters or other types of analysis. In order to test that idea, the following chapter will employ the catalogue in a different way. Rather than looking at one character in different media over time it will look at two different characters over time in a single media, and use the results as a way to uncover new information about their similarities and differences.

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46. 1994. Fantastic Four: The Animated Series. Directed by Marvel Films. Produced by Marvel Films. 47. 2007. Fantastic Four: Rise of the Silver Surfer. Directed by Tim Story. Produced by 20th Century Fox. 48. Skeates, Steve, Joe Albelo, Terry Austin, John Buscema, Dave Cockrum, Armando Gil, Larry Hama, Alan Kupperberg, Bob McLeod, and Marshall Rogers. 1982. “Do-It-Yourself Comic Book.” Crazy Magazine (Marvel Comics) 1 (82). 49. D’Arcy, Geraint. 2020. Mise en scéne, Acting, and Space in Comics. Basingstoke: Palgrave Macmillan UK. 50. Lee, Stan, John Buscema, and Joe Sinnott. 1968. “The Origin of the Silver Surfer!” The Silver Surfer (Marvel Comics) 1 (1). 51. Brubaker, Ed, Pablo Raimondi, and Mark Farmer. 2006. “Book Three.” The Books of Doom (Marvel Comics) 1 (3). 52. Wolfman, Marv, Keith Pollard, and Joe Sinnott. 1978. “When Titans Clash!” Fantastic Four (Marvel Comics) 1 (200). 53. Howe, Sean. 2012. Marvel Comics: The Untold Story. New York: Harper. 54. Sacks, Jason, Eric Hoffman, and Dominick Grace. 2017. Jim Shooter: Conversations. Mississippi: University Press of Mississippi, p. 17. 55. Eco, Umberto. 1972. “The Myth of Superman.” Diacritics 14–22. 56. Singer, Marc. 2019. Breaking the Frames: Populism and Prestige in Comics Studies. Texas: University of Texas. 57. Marvel Database. 2011. “Glossary: Sliding Timescale.” 31 August. Accessed January 14, 2021. https://marvel.fandom.com/wiki/Glossary:Sliding_Time scale. 58. Coogan, Peter. 2020. “The Supervillain.” In The Supervillain Reader, edited by Robert Moses Peaslee and Robert G Weiner, 36–61. Mississippi: University Press of Mississippi. 59. Conroy, Mike. 2004. 500 Comicbook Villains. London: Collins & Brown, p. 36. 60. Walker, Ian. 2010. Research Methods and Statistics. Basingstoke: Palgrave Macmillan, p. 71. 61. Field, Andy. 2016. An Adventure in Statistics. London: SAGE Publications Inc, p. 134. 62. Gavin, Helen. 2013. Understanding Research Methods and Statistics in. London: SAGE Publications Inc, p. 76. 63. Leys, Christophe, Christophe Ley, Oliver Klein, Phillipe Bernard, and Laurent Licata. 2013. “Detecting Outliers: Do Not Use Standard Deviation around the Mean, Use Absolute.” Journal of Experimental Social Psychology 49 (4): 764–766. 64. Rousseau, Peter J, and Christophe Croux. 1993. “Alternatives to the MedianAbsolute Deviation.” Journal of the American Statistical Association 88 (424): 1273–1283. 65. Glut, Don, Fred Kida, and Dave Simons. 1980. “What If Doctor Doom Had Become a Hero?” What If? (Marvel Comics) 1 (22). 66. Gillis, Peter, Tom Sutton, and Bruce Patterson. 1979. “What If Dr Strange Had Been a Disciple of Dormammu?” What If? (Marvel Comics) 1 (18). 67. Byrne, John. 1982. “Too Many Dooms.” Fantastic Four (Marvel Comics) 1 (246). 68. Shooter, Jim, Mike Zeck, and John Beatty. 1984. “A Little Death...” Marvel Super-Heroes Secret Wars (Marvel Comics) 1 (6).

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69. Hibbett, Mark. 2017–2021. Marvel Age Doom (process blog). Accessed December 22, 2020. http://mjhibbett.co.uk/doom. 70. Gaddis, John Lewis. 2011. The Cold War. Penguin. 71. Fortune, Robert. 2015. Tito. Ten Short Chapters. 72. Costello, Matthew J. 2009. Secret Identity Crisis: Comic Books and the Unmasking of Cold War America. New York: Continnuum, p. 28. 73. Englehart, Steve, Herb Trimpe, and Pablo Marcos. 1976. “Who is...the Shroud?” Super-Villain Team-Up (Marvel Comics) 1 (7). 74. MacManus, Steve. 2016. The Mighty One: My Life Inside The Nerve Centre. Oxford: Rebellion Publishing. 75. Bennion, Jackie. 2007. “Frontline/World.” Accessed August 8, 29, 2017. http:// www.pbs.org/frontlineworld/rough/2007/08/philippines_parlinks.html. 76. Byrne, John. 1982. “This Land is Mine!” Fantastic Four (Marvel Comics) 1 (247), p. 14. 77. Reagan, Ronald. 1983. “Address to the National Association of Evangelicals (‘Evil Empire Speech’).” Virginia: Miller Centre for Public Affairs. 78. Friedrich, Mike, Arvell Jones, Keith Pollard, and Dick Ayers. 1975. “The Modok Machine!” Iron Man (Marvel Comics) 1 (74). 79. Palmer, David K. 2016. “The Tail That Wags the Dog: The Impact of Distribution on the Development and Direction of the American Comic Book Industry.” In Cultures of Comics Work, edited by Casey Brienza and Paddy Johnston, 235–249. New York: Palgrave Macmillan. 80. Simon, Joe. 2011. My Life in Comics. Kindle. London: Titan Books. 81. Evanier, Mark. 2009. “News From Me: People of Color.” 6 February. Accessed December 21, 2020. https://www.newsfromme.com/2009/02/06/people-ofcolor/. 82. Cox, Carolyn. 2017. “The Mary Sue: The Surprising Origins of Wonder Woman.” 7 February. Accessed December 21, 2020. https://www.themarysue.com/portal ist-origins-of-wonder-woman/. 83. Lee, Stan. 1976. Bring on the Bad Guys: Origins of Marvel Villains. New York: Simon & Schuster, p. 11.

CHAPTER 5

A Tale Of Two Menaces

This chapter will demonstrate that the unified catalogue of transmedia character components can be used to analyse characters other than Doctor Doom by moving on from ‘The Master Of Menace’ to an examination of the British and American characters who share the name ‘Dennis The Menace’. Both characters are young boys and, in one of the comics world’s greatest coincidences, both first appeared on the same day, 12 March 1951 [1]. There the similarities begin to fade. As Fig. 5.1 shows, the British Dennis is a ‘protopunk-rock-hooligan’ in a black and red jumper who is constantly causing mayhem and chaos, whereas the American version is a wholesome, inquisitive child who represents ‘the irrepressible energy of a young republic’ [2]. DC Thomson’s character Dennis The Menace is the star of the UK’s longest running comic The Beano. The Beano remains a British institution, first published in 1938 and a part of British cultural life ever since [5, 6]. Today it is one of the top-selling British comics, although its current circulation of 54,801 weekly copies and approximately 659,000 readers is tiny compared to its peak of 2,000,000 weekly copies and estimated 16,000,000 readers [7]. The American Dennis was created by Hank Ketcham, based on his own son [5]. This version began as a syndicated newspaper strip and rapidly became popular, achieving syndication in over 300 newspapers within two years [8]. Over the next few decades this Dennis would go on to have his own comic book, TV sitcom and movie adaptations. In Kid Comic Strips: A Genre Across Four Countries Ian Gordon uses the differences between these two characters as a way to explore the differences in British and American comics [9]. Specifically he examines so-called kid comics—that is, comics with a child as the lead character—in terms of their history, form and readership, contrasting the middle-class sitcom nature of the © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 M. Hibbett, Data and Doctor Doom, Palgrave Studies in Comics and Graphic Novels, https://doi.org/10.1007/978-3-031-45173-7_5

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Fig. 5.1 Dennis The Menace from the UK [3] (left) and USA [4] (right)

American Dennis with the more surreal working class slapstick of the British [9]. Gordon concentrates his analysis on the first decade of both strips, and though the main focus is in the areas mentioned above, he does also identify several components that fit into the same categories as the unified catalogue of transmedia character components. For example, he identifies objects such as cars, fences, windows (often broken) and baseballs as appearing in the US strip, with footballs and catapults cropping up more often in the UK. Similarly he identifies characters in the US such as Dennis’s parents, the Wilsons and his friend Margaret, with the UK Dennis similarly also featuring parents along with policemen, grandparents and this Dennis’s arch-enemy Walter. The remainder of this chapter will delve further into these differences and similarities by using a slightly simplified version of the methodology in order to compare the two characters across time.

The Sample As the primary purpose of this exercise was to test the unified catalogue of transmedia character components, several simplifications were made to the surrounding methodology in order to make the data gathering easier. The biggest simplification was the decision to take all of the examples for the British Dennis from one book, The Beano: The Dennis Collection [10]. This volume was published as part of the 70th Anniversary celebrations of the character, and contains several strips from each decade, chosen by the book’s editors. Clearly this selection means that the strips were not chosen at random, and indeed

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reading through the book it became clear that several editorial decisions had been made, notably around corporal punishment. The British Dennis The Menace was regularly punished by his father with a ‘slippering’, i.e. being beaten with a piece of footwear, although this was gradually phased out in the years leading up to 1986, when the practice being outlawed in UK law [6]. During the 1970s, for example, this happened in 35% of all issues of The Beano, and yet it does not feature anywhere in The Dennis Collection [11]. There are a few strips where a physical punishment is implied, but it is never shown. While this is understandable for a book published as entertainment in the twenty-first century, it does mean that it is not a wholly representative sample of what actually occurred in the comics. Other self-censorship can also be expected. For example, along with physical violence towards children Ian Gordon mentions several racial characterisations that would not be acceptable to a modern audience, notably an episode in 1954 where Dennis receives a ‘blackface disguise’ as a present [9]. Issues such as these would make using such a volume impractical for a larger study but, as this analysis is primarily designed as a test of the tool itself, it can be used so long as these caveats are always borne in mind. In order to conduct a fair comparison any sample of British Dennis strips would have to be matched as closely as possible to American Dennis strips from the same dates. Initially finding American strips was more difficult as there was no equivalent volume available, and although the strips had often been collected in the past they were long out of print by the time of this study and very difficult (and expensive) to source in the UK. However, this problem was solved by using the website ‘newspapers.com’, an online resource containing scans of ‘841 million + pages of historical newspapers from 26,700 + newspapers from around the United States and beyond’ [12]. By searching for US newspapers on specific dates, and using the key phrase ‘Hank Ketcham’, I was able track down several versions of the same strip on any day. With these data sources identified I decided to attempt to look at strips taken from roughly ten-yearly intervals, begin in 1951 and then moving on to around 1961, 1971 and so on. This was done purely for convenience, as there were seven decades of strips to go through. For each year a random date was selected by asking a computer to choose a random number between 1 and 365 (none of the years selected were leap years). For example, the first number produced was 160, and so as the 160th day in 1951 was June 9 then the first date required for the sample was 9 June 1951. This raised a further problem with the source corpus, as there was not an equivalent strip for this date in the collected volume of British strips. This would have been unlikely even if the book had reprinted every single strip, as The Beano was published weekly and so there would only be a 1 in 7 chance of a random date matching a publication day, and so instead I chose to use the closest date available to the one randomly chosen. In the example above the closest strip in the book was the one dated 5 May 1951.

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Table 5.1 Dates for sample of UK and US Dennis The Menace strips

UK

US

05/05/1951 21/01/1961 15/08/1970 03/05/1980 21/09/1991 07/10/2000 23/01/2010 08/01/2020

13/01/1952 22/01/1961 16/08/1970 04/05/1980 22/09/1991 08/10/2000 24/01/2010 05/01/2020

In America the daily Dennis The Menace was usually only a one or two panel gag strip, so I decided to use the Sunday strip as a comparison as, with its longer duration and comics-style narrative format (using panels, word balloons and so on), it not only contained more information about the character and their world but was also closer in style to the UK version. It was still shorter than the British strip, containing an average of 10.25 panels per story compared to 17.87 in the UK, but this still provided more detail than the daily version. For the most part I was able to compare every weekly UK Dennis with an equivalent US Sunday strip, with the only exception being the first date in the sample list. Here a US Sunday strip did not exist because it did not begin until 1952, and so the closest text (the first Sunday strip) was used instead. Apart from this I was able to reliably match the two versions and was able to produce a sample containing the 16 texts shown in Table 5.1.

Data Entry With the sample selected the next stage was to examine and enter data for each text in terms of the thirteen dimensions of the unified catalogue of transmedia character components. As with the study of Doctor Doom, this was done by reading through the sample looking for one component at a time, a task made much easier by this sample being smaller both in terms of the number and length of texts. Marvel comics during ‘The Marvel Age’ tended to have around 20 pages of story per issue whereas the UK Dennis’s strip in The Beano varied being between one and three pages long, while the US Dennis’ Sunday strip was never longer than a single page. For this analysis data was entered directly into an Excel spreadsheet, rather than a database, with a separate sheet for each dimension. This was possible because the smaller sample size and shorter, simpler, texts meant there was much less data to be recorded. For example, whereas Doctor Doom existed in a vast storyworld full of many interlocking stories, characters, objects and past events to record, both Menaces existed in largely self-contained worlds with

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much fewer items. Very occasionally the UK’s Dennis would interact with other characters from The Beano but this was rare, and the US version never strayed outside his own tightly controlled world. The simplicity of both characters and their worlds, combined with the fact that the main objective of the exercise was to test the unified catalogue, meant that it was not deemed necessary to conduct a survey of fan opinions before beginning data collection. The descriptions provided by Ian Gordon were used as a starting point instead, and these would prove to be more than adequate for the task. Once the data was collected, cleaning was carried out as before, and again this was a much simpler task as the number of components in each dimension was lower and varied less. In practice this meant that the majority of information for each dimension could be viewed on a single screen which made data cleaning significantly easier and quicker to carry out. The finalised dataset is available to view online at https://doi.org/10. 25441/arts.22708744. Once this process was completed the data was analysed as follows.

Character-Specific Components Appearance As with all components, entering data on this dimension was easier than it was for Doctor Doom because the texts were much shorter. The process was also made simpler by the fact that both versions of Dennis were the main characters in their strips, which meant that they appeared in the majority of panels, often in full-body shots, so there was no problem with any part of them not being visible. Table 5.2 shows all aspects of appearance that were recorded more than once, ordered by the total number of times each one appeared across all texts. This table shows in blunt terms how very dissimilar the two characters were in appearance. The only aspects of appearance they had in common were that they both wore striped tops, were usually quite short (although the British Dennis’s height did vary in his early decades) and usually wore shoes (although the American Dennis’s feet were more often covered by the bottom of his dungarees). Another thing they had in common was that they were both remarkably consistent over time, with the majority of their main signifiers being present in all texts. Neither character had striped tops in their very earliest appearances, although the British Dennis had adopted his red and black jumper by the time of the first text in this sample and the American Dennis would get his striped shirt not long after, along with his fringe and freckles. After that the only major change that occurred for the American Dennis was that the shape of his head occasionally changed, but otherwise he would remain the same across

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Table 5.2 Appearance

Appearance

UK

US

Striped jumper or t-shirt Short stature Shoes Black spiky hair Blond hair Black shorts Knobbly knees Eyebrows down over eyes Socks rolled down Dungarees Fringe over eyes Freckles Medium height Shoes not visible

8 5 7 8 0 8 8 8 8 0 0 0 3 0

7 8 4 0 8 0 0 0 0 7 7 7 0 3

the decades—so much so that it was often impossible to date the strips just by looking at them. This was not the case with the British Dennis, where the style of his illustrations changed noticeably over time, from a rough and ready vibrancy in the 1950s and 1960s to a (subjectively) fairly bland consistency from the 1970s through the 1990s, before becoming more cartoonish in the twenty-first century. These sorts of changes to artistic style were not captured particularly well by this tool and, as will be discussed later, this might be something that could be improved in future. Names and Titles Capturing this information was very straightforward. The characters were almost always named as ‘Dennis The Menace’ in the strip’s title, except for the final strips of the UK version where the story was renamed ‘Dennis and Gnasher’. Within the strip they were referred to as ‘Dennis’ (7 times for UK, 4 for US). No other specific names were used, with the only other descriptions being one-offs. These were all derogatory (e.g. ‘Horrible little boy’) for the British Dennis whereas the American Dennis was referred to in terms such as ‘dear’ by Mrs Wilson. As later components will show, these descriptions of either character were part of a wider story about how they were thought of and described within their individual storyworlds.

5

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Physical Actions The brevity of both texts and sample meant that this component could be gathered by noting down every action that occurred within the two strips. This process generated 41 different physical actions for the British Dennis and 27 for the American. This higher number of actions in the UK was not surprising as the British strips always felt more action-oriented strip while the American tended to rely on wordplay or jokes. However, the fact that this impression was backed up by the analysis is another example of the way that the catalogue can be used to provide quantitative evidence for qualitative evaluations. The different actions were recoded into 17 categories, producing the data shown in Table 5.3. These results show that the two characters had very different relationships with the adult world. American Dennis was in constant dialogue with adults, asking questions and making remarks as (in his mind at least) an equal, whereas British Dennis never did this. Although he sometimes tried to subvert the existing order through violence and destruction, in the main he was subservient to adults and often tried to help them. His world was one of violence, especially towards animals but also towards himself, as he was physically hurt in the majority of his stories. The American Dennis was punished in one story by being made to sit in the corner, but otherwise appeared to be in a commanding position, driving the narrative rather than being driven by it. Both characters were engaged with the physical world, running, eating, playing games and interacting with other characters. Their solo leisure time, however, was spent differently, with British Dennis preferring to read while Table 5.3 Physical actions

Action

UK

US

Overall

Seeks answers Runs somewhere Attacks/scares animals Is hurt Damages property Eats food Makes inappropriate comments Plays trick on someone Reads Watches television Builds snowman Chased by adult Helps adults Hides from someone Plays game Shouts Other

0 3 4 4 1 1 0 2 3 0 1 2 2 1 1 0 8

6 2 0 0 2 2 3 1 0 3 1 0 0 1 1 2 2

6 5 4 4 3 3 3 3 3 3 2 2 2 2 2 2 10

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American Dennis watched more television. It was a surprise to find British Dennis spending so much time reading—both books and copies of The Beano itself—when one might imagine that he would have seen this as an activity for ‘softies’. These results back up the findings of separate studies of both characters. Christopher J. Thompson identifies violence as central to the British Dennis [11]. As shown in Fig. 5.2, violence occurs in the majority of this Dennis’s stories and is used as a driving force both for narratives and ‘determining the nature of interactions and relationship between most of the comic’s characters’ [13]. Henry Jenkins, meanwhile, sees the American Dennis as designed to be seen ‘less as a bad boy than as acting on his natural impulses, seeking to do good for others’ [15]. This Dennis may sometimes be punished—occasionally with a spanking, but much more often with admonition or being told to sit in a corner—but overall he is viewed as someone who ‘exemplifies his culture’s expectations about what it means to be a boy, what it means to be white, and what it means to be American’ [16]. Interestingly, the British Dennis was never physically punished in any part of the sample, or indeed any of the stories in the book from which the sample was derived, despite this being identified as a regular occurrence in the wider corpus of his adventures [9, 11]. The Dennis Collection does feature a few occasions where he is threatened with a slipper, and one story in which Dennis and his friends appear to have recently been beaten, but the actual violence itself

Fig. 5.2 British Dennis receives a beating from his father [14]

5

Table 5.4 Dialogue recoded

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Dialogue

UK

US

Overall

Speaks to adults as equal Self-narrates Asks questions Exclamation of surprise Ya/Yep Abbreviates word endings Exclamation of disappointment Har-Har Huh Speaks to adults as child Oh no/Oo-er/Oops Verbalised laughter Addresses reader Uncertainty

3 8 0 5 1 0 4 4 3 4 3 3 2 1

6 0 6 0 4 4 0 0 1 0 0 0 0 1

9 8 6 5 5 4 4 4 4 4 3 3 2 2

is never shown. It is likely that the editors of the collection took the decision not to show this, as The Dennis Collection was designed to be a celebration of the character rather than a representation of the stories over time. Dialogue There were 33 different items of dialogue recorded, most of which only appeared in one or two texts, and so these were recoded similarly to Appearance items, with the results shown in Table 5.4. British Dennis’s speech was littered with exclamations, whether of laughter (‘Har-har’, ‘Chortle’, ‘Haw haw’), surprise (‘Yikes’, ‘Erk’) or disappointment (‘Bah’, ‘Wah’) in reaction to events happening outside of his control. In contrast, American Dennis constantly asked questions, leading the narrative as he dictated the conversation. This again shows the very different relationships each character had with adults and the world around them. British Dennis tended to talk mostly to himself and when he spoke to adults he treated them as his superiors, at least until the 2000s when these relationships began to become more equal. However, as Fig. 5.3 shows, American Dennis spoke to adults as equals and his dialogue regularly featured abbreviated words (e.g. ‘an’ instead of ‘and’) used in a confident, adult-aping manner. Character-Specific Summary The analysis of these components backs up many of the arguments elsewhere about the very different types of storyworld that each Dennis existed in. This might be partly explained by the different audiences that each strip

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Fig. 5.3 American Dennis talks to Mr. Wilson as an equal [17]

was intended for, with The Beano being squarely targeted at children, whereas Hank Ketcham’s strip had a wider mixture of adults and children [9]. With this in mind it could be argued that the American strip presented an idealised version of childhood as a safe place where adults were to be trusted whereas The Beano represented a child’s point of view, existing in a frightening and surprising world that they had little control over. Both strips were, of course, created by adults, but they still took very different approaches to the way a child’s experience was presented, a difference which will be demonstrated further by the examination of the storyworlds they existed in.

Storyworld-Specific Components Other Characters Unlike Doctor Doom, both Menaces existed in straightforward worlds where each week a single self-contained story was told, with very little happening that did not directly involve the lead character. This is shown by the fact that of the 35 different characters who appeared across the two series, only three did not directly interact with the main character. These non-interacting characters all appeared in The Beano and all took part in the main plotline, rather than the parallel sub-plots which characterised the Marvel storyworld. Table 5.5 shows that only five other named characters appeared more than once in the American Dennis The Menace, and although none of them appeared in more than half of the strips they did occur repeatedly throughout the time period. Only one of these characters was another child, with the others all adults. As Table 5.6 shows, this was very different to the cast of the British Dennis’s stories.

5

Table 5.5 Other characters (US)

Table 5.6 Other characters (UK)

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Character

Strips

Mum Dad Mr. Wilson Mrs. Wilson Margaret

4 4 4 3 2

Character

Strips

Gnasher Dad Other adult(s) Mum Walter Bertie Spotty Cat Lion Other child(ren) Zookeeper

6 5 5 4 4 3 3 2 2 2 2

Here there were ten named characters of whom only four were adults, with the others being either animals or fellow children. The number of animals can partly be explained by the fact that Dennis visited a zoo twice during this sample, but other animals featured in several stories, particularly Gnasher the dog, who was a co-star from the 1970s onwards. Another Beano character, Minnie the Minx, made a guest appearance in the 2020 story. This shows that the British Dennis did exist in a shared storyworld (‘Beanotown’) even though it did not form a large part of most of his stories. References to other Beano characters have appeared throughout his history, with Minnie first crossing over from her own strip in 1958, but these have become more prevalent in later years, with the modern Beano often featuring longer stories that carry on throughout the issue, involving multiple characters [6]. It is clear from this analysis that the British Dennis has a wider storyworld than his American counterpart, and this may in part be due to each story’s format. The American Dennis The Menace was a Sunday strip with an average of 10.25 panels per story, whereas the British version was usually at least a full page long, averaging 17.87 panels per story, making it almost twice as long and so having more space for other characters. However, even taking this into account the UK version was more complex, with an average of 6.87

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characters per strip meaning there were 0.38 characters per panel, compared to 2.37 characters per strip for the US and 0.23 per panel. Looking at the characters in this manner suggests that the UK strip, meant for children, was in some ways more complex as a storyworld than the US one designed to be read by adults. As will be shown, this was also the case for other storyworld-specific components. Objects The world of the British Dennis contained more objects overall (40) than the American (16), averaging 5 objects per strip (0.28 per panel) against 2 per strip (0.19 per panel) for the US. As with the analysis for other characters, this shows that the UK version of Dennis lived in a busier, more complex storyworld. Table 5.7 shows the objects that appeared twice or more overall across the sample. With the exception of the animal cage that appeared during the British Dennis’s zoo visits, these objects were all domestic items, illustrating the fact that both characters exist in ‘the real world’. Objects were almost always interacted with by the main characters, with the exception of the top two. Living room furniture was interacted with by the main character roughly half the time (2 for UK, 3 for US), while newspapers were never interacted with by either Dennis, instead being used exclusively by the British Dennis’s dad in the UK and Mr. Wilson in the US. Ian Gordon’s claim that both menaces existed in similar class settings is at least partially borne out here, with both characters enjoying access to television and cars [9]. However, the British Dennis interacted with books, comics and Table 5.7 Objects

Object

UK

US

Living room furniture Newspaper Television Book Car Tree Cookie jar Snowman Bag Cage Computer Hat Knitting The Beano

3 1 1 4 1 2 0 1 2 2 2 2 2 2

6 3 3 0 2 1 2 1 0 0 0 0 0 0

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computers in a way that his US counterpart never did. Where the American Dennis consulted with his parents for information about the world, British Dennis preferred to find it out for himself. This trend is apparent throughout the data here, and I wonder if this, more than anything else, demonstrates a cultural difference between the UK and USA, or at least a difference in how each country likes to think of itself, with British children left to find their own way as mini-autodidacts, while American children felt they could access information via adults. Settings The number of different settings was similar for both characters, with British Dennis averaging 0.19 settings per panel and American Dennis averaging 0.18. Table 5.8 shows settings that appeared twice or more. As with objects, these settings were all very domestic, usually taking place within the character’s own home (including their garden) or their neighbour’s house. British Dennis was slightly more likely to be outside than inside, while American Dennis spent more time in a neighbour’s house. As with other components, almost all settings featured the lead character, with the main exception being the Wilson’s house, which on two occasions was occupied only by Mr. and Mrs. Wilson.

Table 5.8 Settings

Setting

UK

US

Living room Street Garden Wilson’s living room Countryside Blank location Hallway Kitchen Wilson’s car Wilson’s front door Dennis’ room Playground Shop Walter’s garden Zoo

4 5 3 0 2 3 1 2 0 0 1 2 2 2 2

4 1 2 4 2 1 2 1 2 2 1 0 0 0 0

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Previous Events Across both texts there was only one reference to any kind of past event, in 2020 when British Dennis recalled previous experiences of exploring. We find something amazing like a crystal skull, forgotten tunnels, dinosaurs, trolls, witches, time machines, buried treasure, ghosts, zombie pirates or conkers, but there’s nothing today! [18]

This is the only time that either version of the character refers to there even being a past, let alone mentioning any specific occurrences. As stated previously, although British Dennis does exist in a shared storyworld it does not play a large part in his stories, and both strips tend to exist as one-offs that can be enjoyed by readers without previous knowledge. This makes them examples of Eco’s ‘oneiric climate’, with their components remaining the same from episode to episode with little or no relationship to past or future events [19]. Interestingly, British Dennis’s storyworld has come into greater focus in recent years, notably around the character’s 70th birthday in 2021. Here a special strip was created to explain how he changed from the grey shirt and tie of his first appearance to the more well-known red and black jumper, and a ‘Menace family tree’ was produced which, for the first time, made clear that not only was the current Dennis the son of the Dennis from the 1970s and 1980s, but that that Dennis’s father was the original Dennis from the 1950s. It was also clarified that Minnie the Minx was Dennis’s cousin and, perhaps most mind-blowing of all for British comics readers, it was hinted that his mother was the original Minnie. In some ways this turn of events was similar to what happened during the Chaos period at Marvel, when comics fans took control of the characters and started to play with continuity. This took place half a century later in the UK and, as will be shown with authorship, this was not the only aspect of comics culture that took a lot longer to reach DC Thomson than Marvel. Storyworld-Specific Summary The analysis for these components again shows that there were differences between the storyworlds of the two characters, with that of the British Dennis continuing to be more complex than the American, and the latter having more engagement with adult characters than the former. Both storyworlds were heavily focused on the main character and their existence in the present, with no sub-plots, no ongoing storylines, and almost no recognition that past stories existed. This was in extreme contrast with Doctor Doom’s storyworld, where he was usually a minor character passing through a complicated, multi-textual world where no single character was the sole focus of the wider story. The unified catalogue of transmedia character components proved to be adept at analysing both.

5

Table 5.9 Perceived behaviour for Dennis The Menace recoded

A TALE OF TWO MENACES

Heroic Villainous Positive Negative Strong Weak

197

UK

US

0 8 1 7 8 0

3 3 3 3 5 1

Behavioural Components Perceived Behaviour The way that the two characters are described reinforces many of the impressions already gained from other components, with the British Dennis being much more negatively described, and more often, than his American counterpart. The British Dennis was described in eight different ways over six texts, as opposed to six different descriptions in four texts for the American Dennis. British Dennis’s descriptions were overwhelmingly negative (e.g. ‘Horrible’, ‘Atrocious’) whereas American Dennis’s were a mixture of positive (‘good little boy’) and negative (‘pathetic’). The differences between the way that the two were described can be shown more clearly by regrouping the descriptions into groupings similar to those used for Doctor Doom—Heroic/Villainous, Positive/Negative and Strong/Weak—as shown in Table 5.9. British Dennis was overwhelmingly perceived as villainous, negative and strong, whereas the perception of American Dennis was much more mixed throughout. Indeed, he was often described in contrasting ways during the same strip. This further demonstrates the different sorts of storyworlds that the characters exist in, with the British Dennis taking a child’s view of being oppressed and looked down upon, while his American counterpart presented a more optimistic, or possibly nostalgic, view of children as being cherished by adults. BFI As with Doctor Doom, the experience of entering data for this component felt particularly ad hoc and unreliable, with the constant feeling that these were ‘just my opinions’. However, also as with Doctor Doom, the eventual results generated interesting information that was in line with other findings. Table 5.10 shows more similarities between the two characters than in other components. Both scored highly for extraversion and had similar scores around the middle of the scale for agreeableness and openness—results which tallied with subjective opinions of the contents of the texts.

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Table 5.10 BFI scores (with MAD)

UK US

Extraversion

Agreeableness

Conscientiousness

Neuroticism

Openness

8.12 (1.63) 10 (0.00)

5.75 (0.75) 5.75 (1.00)

7.25 (1.75) 5.875 (1.38)

6.25 (2.50) 3 (1.00)

4.37 (1.13) 4.87 (0.63)

The two characters differed in their scores for conscientiousness and neuroticism. American Dennis’s middling score for conscientiousness is reflective of the fact that while he did sometimes work out and follow through his own plans, he was less careful about activities that affected other characters, such as cleaning his room. British Dennis, on the other hand, is shown here to be very diligent, surprisingly so given his reputation with other characters as an agent of chaos. British Dennis also scored much more highly for neuroticism, whereas American Dennis rated lower for this measure than any other. This also ties in with the general conception that arises from this data, with American Dennis existing in a cosy storyworld where he was in control of his surroundings, while British Dennis was more a victim of events, often surprised and alarmed by them. The MADs for each character show that American Dennis was generally more consistent than his British counterpart, with lower MADs in all cases except for Conscientiousness. Indeed, American Dennis’ MAD score of zero for Extraversion shows that he was exactly the same in this regard in every text. American Dennis had a lower MAD than Doctor Doom in all scores apart from Conscientiousness as well, whereas British Dennis was less stable than both characters in all categories except Agreeableness, where his MAD was slightly lower than the other two. One might expect this result, as American Dennis has been presented as the work of a single creator for his entire publishing history, while his British counterpart has been allowed to change over time with different creators, even though they were almost never credited. It does seem counter-intuitive, however, that British Dennis was less coherent than Doctor Doom over time. He did have different creators over time, but these changed slowly over more than seventy years, whereas Doctor Doom had hundreds of different creators in a sample that spanned less than half the time. It could be argued that this demonstrates a cultural difference, with US characters being more tightly controlled because they are used across more formats, transmedially and transtextually, than their UK counterparts, and thus needed to be kept recognisably the same across all texts. On the other hand it might simply be that UK comics companies were less concerned about maintaining character coherence, so long as material was produced on a weekly basis.

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Table 5.11 Menace motivations Personal gain Get information Resist authority Seeking leisure Harm others Help others

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UK

US

4 0 4 2 3 2

3 5 1 2 0 0

Motivation Once again, British Dennis was shown to be more complex than American Dennis, with twice as many different motivations (18 versus 9) recorded across the sample. However, as Table 5.11 shows, once these were recoded into broader categories the two characters were driven by roughly the same number of different motivations. Both menaces were concerned with personal gain—attempting to make money, win prizes, or get food—and leisure pursuits such as watching television or reading. American Dennis was, once again, primarily focused on getting information about the world around him, always from adults, while British Dennis was more driven by a need to defy authority, as well as seeking either to harm or help other characters. Looked at in this way we can see that both Menaces were mostly driven by personal, possibly selfish, needs. The difference between them came from their attitudes to the outside world, with American Dennis looking for answers about how it works while British Dennis preferred to interact with it directly. Here we can view the American menace as a thinker while the British menace is more of a doer. Behavioural Summary There were some behavioural similarities for the two characters, with both driven by self-interest and living very much in the ‘now’ with little or no interest or knowledge of the past. However, they were mostly very different, and sometimes almost opposite in their behaviour, with American Dennis a confident, cherished child who was the centre of his own world and rarely changed, while British Dennis was neurotic, buffeted by events, openly described in negative turns, and even more prone to changes in character than The Lord of Latveria. As in previous component groups, these findings demonstrate that the unified catalogue of transmedia components is a highly effective tool for gathering quantitative evidence that illustrates key differences in these characters.

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Authorial Components Market Authors Information about Market Authorship was sparse in both cases, with half of each set of texts containing no information at all. For the American Dennis The Menace the market authors were Field Enterprises (1980), King Features (2000), North American Syndication (2000–2020) and Comics Kingdom (2020). In The Beano the market authors were DC Thomson (1970–1991) and CBBC (2010), with this latter being linked to an animated series that was being broadcast at the time. These results were very different to those for Doctor Doom, most likely because both Menaces appeared in strips that were part of a larger text, whether a newspaper or anthology, rather than a traditional single story American superhero comic. British Dennis appeared on the front and back cover of The Beano from 1970 to 2000 and so the indicia crediting the entire comic to DC Thomson appeared on these strips. Otherwise there was little information. It could be argued that ‘The Beano’ could be considered a market author in the same way that ‘Marvel Comics’ played that role for Doctor Doom, but here ‘The Beano’ was the title of the comic itself, with no inference that it was an organisation or group of creators. Instead it acted in a similar way to a title such as Supervillain Team-up, describing the contents rather than claiming authorship over them. The American Dennis appeared in multiple different newspapers alongside an enormous range of other strips, news and features, all varying from paper to paper and across time, with no suggestion that any individual newspaper claimed market authorship. Instead market authorship was contained within the small lines of text placed within the gutters between panels, usually denoting which syndicate had supplied it. There might be an argument for the statement ‘by Hank Ketcham’, which appeared at the top of every strip, being seen as a form of market authorship, not least because he died in 2001 and so could not possibly have exerted actual textual authorship over later stories. However, taking the same philosophy as before of relying solely on what is presented within the text itself rather than any exterior knowledge, this credit was instead entered as textual authorship. Textual Authors Hank Ketcham retired from the American Dennis The Menace strip in 1994, but he was still credited as sole author in the 2000 text included in the sample. His former assistant Ron Ferdinand was credited in 2010 and 2020, although in smaller text secreted within the panel borders rather than on the masthead, and he was joined there in 2020 by Ketcham’s son Scott. There were no textual authors at all for the British Dennis until the final text in the sample, in which Nigel Auchterlonie and Nigel Parkinson were credited

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as writer and artist respectively. This was in line with DC Thomson’s longstanding policy of not crediting its creators, supposedly to prevent them being poached by other companies [6]. This policy was widespread across British children’s publishing for most of the twentieth century, and only began to change in 1977 when Kevin O’Neill suggested including ‘Credit Cards’ in 2000AD, published by DC Thomson’s main rivals IPC [20]. The practice then very gradually spread, although it is ironic that the publishing of creators’ names had exactly the results feared, with a large number of British creators in the 1980s being lured overseas to work for American companies [5]. Authorial Summary Very little data was available for either character for either authorial component, but this fact tells its own story about the relative importance of authorship to the markets involved. For Marvel comics, the fact that they were Marvel comics was seen as a specific selling point, and the names of companies and creators were used similarly. For the two Menaces, however, it appears that the characters rather than their real-world creators were seen as more important.

Overall Analysis Using the unified catalogue of transmedia components to examine the different menaces demonstrates two key points. Firstly it proves the (admittedly obvious) point that, despite the shared name and date of creation, these were two very different characters. If forced to broadly summarise, one could say that the American Dennis was something of a spoiled brat, catered to by adults who existed solely to feed his curiosity and be charmed by his impudence. The British Dennis, however, was ignored or dismissed as a nuisance by adults and existed in a frightening, surprising world over which he had little control. Indeed, this analysis made me reappraise my own views of the British Dennis, a character I had known my entire life. It forced me to consider whether his desire to cause chaos and seek harm to others could be put down to the constant negative reinforcement he received from authority figures and the lack of stability or calmness in his world. Earlier I stated that the use of this model should not be confused with a psychological analysis of a ‘real’ person, but here it is very tempting to tie this into the later continuity of multiple generations of menace to create a portrayal of a cycle of child neglect. The 1950s Dennis was labelled as a ‘menace’ and so he behaved as one, passing these behaviours onto his own son in the 1970s. In this analysis the lower levels of neuroticism of the twenty-first-century incarnation of the character can be seen as a successful attempt by the second Dennis to break this cycle and raise his own son in a more nurturing environment.

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Whether we indulge in this theorising or not, it is clear that despite both strips being short comics concerning the comedic adventures of young boys, they are very different indeed—as Ian Gordon states, ‘The shared genre does not equate to the sameness or commonality of comic strips’ [9]. Gordon suggests that these differences are due to factors of history, form, and readership, and I would tend to agree that these seem to be the most sensible explanations for these differences. However, the ability to demonstrate this comes about through a combination of Gordon’s theorising and the quantitative data that using this methodology generates. This is the second conclusion that can be drawn from this analysis—that the unified catalogue of transmedia character components proved to be an extremely useful and flexible tool for generating data around these two characters and providing information that could then be used for a deeper qualitative discussion of the underlying meanings behind the differences and similarities shown. It is straightforward, for example, to note the differences in the way the two characters dressed, but it was only through examining how they spoke and behaved around the settings, objects and other characters of their storyworlds that deeper differences became apparent, and further explanations were suggested. The catalogue was able to do this just as well for a small sample of shortform ‘kid comics’ in oneiric storyworlds as it was for a larger sample of much longer superhero comics existing in a complex and ever-evolving shared storyworld. It also worked effectively as a way to generate a straightforward comparison between two texts existing in a single media, even though it was designed to work as a way to compare changes across time and media types. Thus I think it is fair to say that this shorter analysis has shown that use of the catalogue is not restricted to analysing Doctor Doom and can in fact be used much more widely than just examining transmedia characters over time. The next, and final, chapter will discuss some of these potential uses.

References 1. Gifford, Denis. 1984. The International Book of Comics. London: The Hamlyn Publishing Group. 2. Parker, James. 2016. “Dennis the Menace Has an Evil British Twin.” Smithsonian Magazine, March. https://www.smithsonianmag.com/arts-culture/dennismenace-has-evil-british-twin-180958114/. 3. Auchterlounie, Nigel, and Nigel Parkinson. 2020. “Dennis & Gnasher.” BoredomBusting Golden Beano #1 (DC Thomson) 1 (1). 4. Hamilton, Marcus, and Scott Ketcham. 2023. “Dennis the Menace.” (Comics Kingdom). https://comicskingdom.com/dennis-the-menace/2023-04-18. 5. Sabin, Roger. 2001. Comics, Comix & Graphic Novels: A History of Comic Art. London: Phaidon Press. 6. McLaughlin, Iain. 2022. The History of the Beano. Barnsley: White Owl. 7. Beano Studios. 2022. Beano Media Pack. Dundee: D.C. Thomson & Co. https://wpcluster.dctdigital.com/wp-content/uploads/sites/47/2022/10/ Beano-Media-pack-Oct-22-gcsel9xq.pdf.

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8. Jenkins, Henry. 2023. Where the Wild Things Were: American Childhood and the Permissive Imagination (in Preparation). University of Southern California. 9. Gordon, Ian. 2016. Kid Comic Strips: A Genre Across Four Countries. New York: Palgrave Pivot. 10. Beano Studios. 2020. The Beano: The Dennis Collection. London: Bonnier Books. 11. Thompson, Christopher J. 2019. “‘Boiled or fried, Dennis?’ Violence, Play and Narrative in ‘Dennis the Menace and Gnasher’.” In Representing Acts of Violence in Comics, edited by Nina Mickwitz, Ian Horton and Ian Hague, 105-118. New York: Routledge. 12. Ancestry.com. 2023. About Newspapers.com. Accessed March 15, 2023. https:// www.newspapers.com/about/. 13. Thompson, Christopher J. 2019. “‘Boiled or Fried, Dennis?’ Violence, Play and Narrative in ‘Dennis the Menace and Gnasher’.” In Representing Acts of Violence in Comics, edited by Nina Mickwitz, Ian Horton, and Ian Hague, 105–118. New York: Routledge, p. 116. 14. Law, David. 1956. “Dennis the Menace.” The Beano (DC Thomson) 1 (711). 15. Jenkins, Henry. 2023. Where the Wild Things Were: American Childhood and the Permissive Imagination (in Preparation). University of Southern California, p. 117. 16. Jenkins, Henry. 2023. Where the Wild Things Were: American Childhood and the Permissive Imagination (in Preparation). University of Southern California, p. 141. 17. Ketcham, Hank. 1991. “Dennis the Menace.” (King Features Syndicate). 18. Beano Studios. 2020. The Beano: The Dennis Collection. London: Bonnier Books, p. 142. 19. Eco, Umberto. 1972. “The Myth of Superman.” Diacritics 2 (1): 14–22. 20. MacManus, Steve. 2016. The Mighty One: My Life Inside the Nerve Centre. Oxford: Rebellion Publishing.

CHAPTER 6

Discussion

The unified catalogue of transmedia character components brought together aspects from several different theories of transmedia characters, including Pearson and Uricchio’s signifiers, Klastrup and Tosca’s topos, ethos and mythos, Marie-Laurie Ryan’s story/worlds/media definitions, Paolo Bertetti’s existential and fictional identities, and Matthew Freeman’s character-building, world-building and authorship [1–4]. These theories were joined together within a framework based on Jan-Noël Thon’s transmedia character networks, and applied to concepts of characters and storyworlds which, although not necessarily ‘transmedia’ under Henry Jenkins’ strict original definition of transmedia as a single storyworld across which stories are told, do fall under Scolari, Bertetti and Freeman’s wider definition of earlier transmedia characters who carried their storyworld with them [5–7]. Bringing all of these theories together into a unified catalogue created a single resource that contained all aspects of transmedia characters in one place. While there was significant overlap between all of the theories, no one theory on its own contained all of the components identified overall. The crossover between different theories is shown in Fig. 6.1. In addition, replacing some of the individual names given to components with a single naming scheme which avoided unclear or obscure terminology made the catalogue easier to use and understand than was the case with some of the source theories. For instance, terms such as ‘topos’ and ‘mythos’ in Klastrup and Tosca’s original codification would be unclear to anyone without a background either in classics or semiotics, and so are very difficult to use meaningfully—over the course of this research I found myself having to refer back again and again to remind myself what each one actually meant, whereas

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 M. Hibbett, Data and Doctor Doom, Palgrave Studies in Comics and Graphic Novels, https://doi.org/10.1007/978-3-031-45173-7_6

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Fig. 6.1 Theoretical underpinning of the unified catalogue of transmedia character components

the terms ‘location’ and ‘past events’, which the unified catalogue uses in their place, are self-explanatory without losing meaning. One way to demonstrate the effectiveness of this preference for clear terminology is through the dissemination of this research. Whenever this work was presented at conferences lengthy explanations were required for terms like ‘existential proper figurative identity’ or ‘fictional modal identity’, but none have ever been needed when referring to ‘appearance’ or ‘motivations’.1 Using clear, straightforward, terminology increases understanding and makes the catalogue more accessible to researchers without a specialist or privileged background. Bringing these definitions together also identified areas where further clarity was required. As stated, most of the original individual theories upon which the catalogue was based were intended to be used as theoretical models rather than practical tools, often based on the needs of describing a single character or storyworld within a specific research output. This meant that some aspects were left frustratingly vague for anyone wanting to apply them more widely in practical research. The prime example of such unclear definition was the idea of ‘behaviour’. All of the source theories included ‘behaviour’ as a component but did not specify how this could be categorised. Each also described how a character might ‘behave’ in a single text, with a qualitative description of their actions

1

The same applied even more so when explaining it to non-academics.

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within the narrative, but there was no structure given in any of the theories as to how this could be done across multiple texts in a way that could be empirically analysed or compared. Nor was there much agreement about what ‘behaviour’ actually meant, with some theories using ‘behaviour’ to mean the character’s actions, others their motivations or moral frameworks, or combinations of the three. Here grounded theory was very helpful. The issues around the meaning of ‘behaviour’ originally arose during the first iteration of data entry, when I was constantly presented with aspects of behaviour that either did not fit or could not be coded into the existing models. A great deal of time was spent investigating existing codification systems, hoping for a list or look-up table of synonyms for behaviour descriptions which could then be used to boil down the different behaviours I was uncovering into a single, neat codification. No such codification existed. As discussed, this problem of describing behaviours is a recognised issue in the field of psychology which, over the course of almost 90 years, has not yet been satisfactorily solved, and so other strategies were required. After much investigation and experimentation behaviour was separated out into three separate components—perceived behaviour, personality traits , and motivations. Perceived behaviour used a psycholexical approach, noting the way that Doctor Doom’s general behaviour was described by himself, other characters, and within the narrative of the text, while motivations took a similar approach towards the reasons given for why Doom did what he did. Personality traits were recorded using the BFI-10 questionnaire, which was surprisingly useful. I had serious doubts about the questionnaire’s efficacy while entering the data, both for Doctor Doom and the two versions of Dennis The Menace, as if felt like I was just entering personal opinions, but the end results were strikingly revealing, not just for the clear way that they showed characters evolving over time and media but also as a window into the changing attitudes and practices of the textual authors creating these storylines. These behavioural aspects became such a large part of the analysis that they were moved into their own group, separate from their initial placement as a single category within character-specific components. One way of considering the way a character behaves is as the interaction between them and the world they live in, and so separating it out into its own category between character and storyworld was a logical step, expanding on the groupings which were originally described by Matthew Freeman.

The Corpus and Sample In common with other corpus-based studies, the generation of a truly transmedia corpus of texts featuring Doctor Doom faced two main obstacles. The first of these was the unreliability of existing databases. Although the Grand Comics Database was an excellent starting point for the corpus it had

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issues with both incompleteness, where data was missing that could have identified a text as featuring Doctor Doom, and over-completeness, where appearances in advertisements and other paratexts were sometimes included. Either of these problems would have been easier to deal with if they were applied uniformly—if, for example, paratexts were always entered for every text then they could have been part of the wider analysis—but the ad hoc, voluntary nature of the data entry meant that they were not. The comics part of the corpus thus required a great deal of data cleaning, but this was at least relatively straightforward. The second, greater, obstacle was the difficulty in identifying non-comics texts in the absence of similar databases. Some resources for non-comics texts are available—the Wikipedia page Doctor Doom In Other Media was extremely useful, and a professional account with IMDb might, in theory, have enabled searches in that database— but these are by no means comprehensive and there are no databases that list every appearance by fictional characters across books, radio, audio-plays or other such media [8, 9]. This meant that creating the corpus was a lengthy, occasionally haphazard, process which continued throughout the duration of the project. Without a definitive, or even non-definitive, directory of character appearances, and in the absence of any previous academic research about Doom’s various transmedia appearances, the main method for discovering non-comics texts was to use Google to search for variations on the character’s name and media types, using phrases such as ‘Doctor Doom radio’ or ‘Dr Doom audio’. This uncovered texts such as The Fantastic Four radio show and Spidey Super Stories album which do not appear on the Wikipedia list mentioned above. Other items were stumbled upon by accident, whether while googling something else (as happened with the Power Records album Fantastic Four: The Way It Began) or simply by looking at comics-related Facebook groups (which is how Fun and Games Magazine was identified). This was clearly not an ideal way to gather a definitive corpus, especially as these outlying media types were often the most interesting. The novel Doomsday [10], for example, includes several fascinating additions to Doom’s mythology, but was discovered far too late in the process to be included in this analysis. Another problem for the non-comics texts in the corpus was the use of cover dates to define ‘The Marvel Age’ and its sub-periods. While this was extremely useful for the comics texts, giving a clear, easily defined way to decide which should be in the corpus, it was less easy for non-comics simply because they did not have cover dates in the same format. This was solved by giving non-comics texts a nominal cover date the same as that of a comic released on a similar actual date. In practice this meant giving such texts a ‘cover date’ that was three months after the month the text was first released, so that any media item issued between 8 August 1961 (the first date upon which comics with a November 1961 cover date would be available) and 14

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July 1987 (the last day for comics with an October 1987 cover date) could be included. This was obviously not ideal, as it was not always clear exactly when noncomics texts had been released. Cartoons, for instance, could be shown on different dates in different parts of the US during this period, and so this was often approximate at best. Luckily for this particular corpus, these loose dates proved to be sufficient for the task required, with all the dates thus derived being well within the boundaries for the periodisation as a whole and also within the three sub-periods, so none of the texts were at risk of being miscategorised. This was a fortunate result of there not being a huge number of non-comics texts to consider, but for a larger corpus featuring a character with more transmedia appearances this could pose problems, and so any future projects using this methodology would need to set clearer boundaries for inclusion. One way to do this might be to use ‘month of first release’ instead, and derive this for comics from the cover date, so that comics with a cover data of November 1961, say, would all be assumed to have a ‘month of first release’ of August 1961. Similarly a cartoon that was first shown on 9 December 1978 would keep ‘December 1978’ as its ‘month of first release’. With the corpus identified, a further issue arose with the size of the sample for analysis. In an ideal world sampling would not be necessary and every text within the corpus could be analysed, but this is rarely feasible due to time and resource constraints. In my own experience of large-scale epidemiological studies a sample would need to be of sufficient size relative to the overall corpus for any results to have statistical significance. However, the corpus here was so small, at least for statistical purposes, that almost two-thirds of the total corpus would need to be analysed to achieve this, which was again not feasible given the time and resources available. For this reason it was decided to select a sample that was ‘representative’ of the whole, based on time period and media type. This worked well, in that it produced a manageable sample of 69 texts which reflected the overall corpus, containing a balanced representation of the titles and media types for each of the three sub-periods. The main concern with this approach was that the small sample size might make it easier for some things to be missed. For example, as I worked through the complete corpus for the Marvel Age Doom blog I noticed certain specific tropes which seemed important. One early example was Doom jumping out of windows to escape, which happened in several of his early adventures but did not crop up enough in the sample to be part of the analysis. However, a statistician would be likely to point out that this is the methodology working as it should do, and that if jumping out of windows did not appear in the representative sample then it was probably not as common as I had thought. It could be that I was over-estimating Doom’s tendency to do this because it happened a few times in succession right at the start of my reading, and so stuck with me. This is supported by the fact that ‘jumping out

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of windows’ did not appear in any form in the respondent survey. Meanwhile other aspects that I noticed as time went by, notably Doom’s constant appearances at the United Nations, definitely did appear in the sample enough times to warrant analysis, despite also not being highlighted by the survey. This still left the problem of the small number of non-comics items in the final sample. As stated above, this was due to the fact that the sample was selected to be representative of the entire corpus in terms of media type, which meant that the vast majority of texts sampled were comics. The small number of non-comics led to some difficulties in properly assessing the results of the analysis. For instance, Doom calls people ‘Fools!’ in 75% of all non-comics media where he speaks, which is three times as often as the 25% in which he says ‘Bah!’ but the fact that there were only four non-comics texts in which he spoke at all means that it is very difficult to say whether this finding is meaningful. If more texts were included with dialogue it is entirely possible that this figure could change, or that other phrases might appear. For this reason I would suggest that any future studies combat this issue by including a larger proportion of non-comics texts, as long as they were still representative of time period and series within non-comics media. If, say, twice as many non-comics texts were included this would allow for more indepth analysis of these texts specifically, and it would be simple to apply a mathematical adjustment to enable a representative comparison with comics texts. Despite this, the strength of the results produced reassured me that using ‘representativeness’ was a valid method of sampling, although I do think it would be interesting, for future analyses, to attempt to build a sample that could achieve statistical significance and see if the results were any different … especially if somebody else was doing the data entry.

Data Design and Data Entry Once the sample was selected and the unified catalogue of transmedia components was finalised, setting up the data entry forms was fairly straightforward. These were based on a system which I had built to administer another website, using pre-existing forms as templates to save time while the processes were developed and tested. This allowed me to work quickly and nimbly, making changes to the data entry forms as new issues arose, but it does mean that these forms were wedded to my own system and so would not be practically re-usable by future researchers, who would need to set up their own processes instead. Ideally I would have liked to build a re-usable data entry system but doing so would have taken up considerable extra time, and was not part of the remit of the project. The actual data structure behind these forms, however, was designed with data portability in mind. This is the ability for information to be moved ‘among different application, programs, computing environments or cloud services’ [11]. The data tables used were easily exported into other systems,

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such as the Microsoft Access database used for the analysis and the Excel tables which have been made available online to allow others to understand and re-use them [12]. Thus, although the data entry system is not re-usable, the methods used to create them and the data they produced very much are. Getting to this point did, as noted earlier, involve setting up and abandoned an entire earlier version of data entry tables and forms. This was all part of the development process and made a vital contribution to the final design of the catalogue, notably helping to crystallise the way that data entry itself was conducted. For example, an early idea to enter data on texts in random order was very quickly abandoned when attempts to do this proved to be far too confusing. Similarly it soon became clear that going through the entire sample looking for specific components was much more efficient than trying to do all thirteen in the same text, especially for comics texts. By looking for, say, Doom’s physical actions in consecutive texts I was able to train myself in what to look out for, removing the need to keep reminding myself of what had gone before. Thus, for the second iteration of the system I set up a list of sample texts on Google Docs, with links for each to their place on the Marvel Unlimited app (where available) and the specific data entry screen on my own site. This was immensely helpful, as it allowed me to work through the entire sample again and again, going from one text to the next without having to search Marvel Unlimited and my own database for the correct links every time. Although working through the corpus looking for specific components in this way worked well for comics, including both versions of Dennis The Menace, it was much less practical for other types of text. The pages of a comic, physical or virtual, can be flicked through quite quickly when looking for components such as objects or other characters, whereas a radio play or cartoon can only be experienced in real time, as fast forwarding ran the risk of skipping past important details. Thus for non-comics texts I developed a practice whereby I would familiarise myself with each text by first watching, reading, or listening to it all the way through without taking notes, then going back through it again, this time taking notes and pausing whenever something appeared, before finally going back a third time to check that nothing had been missed. For all texts, the simplest items to code were those that were explicitly identified in the text itself, such as names and titles, dialogue and—to my surprise—perceived behaviours and motivations. The third part of the new ‘Behaviour’ grouping was also surprisingly straightforward, as the BFI-10 questionnaire proved to be easy to complete and provided a robust way of measuring personality traits. The fact that the behavioural components, which had been so problematic in the early stages of the research, were some of the easiest to record in the final system was further proof that dividing ‘behaviour’ into three separate components and placing them in their own grouping was the correct decision.

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Overall coding for Doctor Doom was also greatly helped by the results of the signifier survey, especially when identifying what should be coded. For example, coding Doom’s cloak and tunic as ‘green’ was obvious, but during the first iteration of data entry I wondered whether the specific shade of green, should also be recorded, as it occasionally got darker or lighter. The fact that the respondents to the survey only ever referred to them as ‘green’ told me that this was sufficient, and that the only time additional coding was required was if either ever changed to a different colour altogether. The coding of Doom’s physical actions was also guided by the survey, with the respondents identifying important signifiers that I would otherwise have missed such as ‘Bolts of energy from hands’ or ‘Shakes or clenches fist/hand gestures’. However, there were other signifiers that did not rank highly in the survey that would appear regularly in the sample, notably ‘looking at monitors/video screen’, and so the survey was always used as a guide, rather than a rigid set of definitions. The most difficult, though often most enjoyable, component to code was ‘Other characters’, especially as the sample moved into the ‘Chaos’ and ‘Consolidation’ periods. This was when the storyworld grew, Doom travelled more widely within it, and writers began to embrace parallel storylines with more characters in them. Some texts, such as Fantastic Four Roast and issues of Marvel Superheroes Secret Wars and its Secret Wars II sequel, had enormous casts of characters, and my skills as a life-long fan were taxed to the limits in identifying them all. Over the course of this research several people have mentioned the possibility of using AI to conduct some of this data entry work. My understanding of the current state of this technology is that it would theoretically be possible to train an AI to recognise individual characters and aspects of appearance, and it would definitely be feasible to use a text recognition system to gather data for an analysis of text-based components such as dialogue, previous events, perceived behaviour and market and textual authors, although this is not yet 100% reliable and would still require checking by human beings. Other components, such as physical actions, locations and personality traits would be much more difficult as these require an understanding of the narrative itself, rather than a textual analysis. There is also the issue of cost—generally speaking, existing AI projects concentrate on very small samples and are grantfunded, rather than unfunded examinations of larger samples as this analysis was. It may be that at some point the process could be automated, with AI systems analysing comics, but until then the cheaper and more available route is the one taken by studies such as What Were Comics ? and The Claremont Run of using research students to do it—or, in this case a single research student: me. Despite the workload involved in entering the data by hand, and some of the challenges with spotting obscure characters, overall the data entry worked well. The fact that the unified catalogue of transmedia character components was focused on the contents of the texts themselves in such a specific way

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meant that it was simple to identify what was required and enter it into the system, guided (but not governed) by the signifier survey responses. As will be discussed next, this straightforward data entry led to a rich dataset.

Data Analysis The key finding of the data analysis was simply that the data entry had generated an enormous amount of data to analyse. The unified catalogue of transmedia character cohesion had already proven to be a successful data gathering tool, and when the final dataset was examined it showed a rich source of information with a wealth of opportunities for analysis. One major benefit of this dataset was that it provided ways to empirically demonstrate ideas that otherwise might only be intuited subjectively. For example, the results went some way to validating my periodisation of the three sub-periods within ‘The Marvel Age’. The expansion of the storyworld over time was clearly evidenced by the results showing that Doom mostly interacted with The Fantastic Four during the ‘Creation’ period, but then associated with a much wider group of characters as the ‘Marvel Universe’ expanded in the ‘Chaos’ and ‘Consolidation’ periods. The way that Doom was described in the texts was shown to be different in each sub-period too, with Doom more likely to be described as heroic as time passed, especially in the ‘Chaos’ period, while the number of terms used per text also peaked during this period, before dropping significantly during the time when Jim Shooter was imposing his own storytelling rules on Marvel’s writers. It is in the Behaviour components, however, that this change was clearest, particularly in terms of neuroticism/emotional stability in comics texts. Here Doom went from scoring consistently in the mid-range for neuroticism/ emotional stability during the ‘Creation’ period, with a median score of 6, before changing dramatically during ‘Chaos’ to a median score of 3, indicating low emotional stability and high neuroticism. Part of this may be due to Doom becoming a lead character, whose mood swings powered storylines, but can also be assigned to the change of textual authors, with new younger creators wishing to explore Doom’s inner feelings more deeply. Apart from this Doom’s behaviour within the first two sub-periods was generally consistent, especially if one ignores his appearances in comics such as Not Brand Echh where he was portrayed differently for humorous purposes, or the simplified version of the character who appeared in Spider Super Stories. In both these circumstances we would expect a slightly different pattern of behaviour and the use of the BFI-10 to measure character cohesion showed that this was the case. The bigger changes occurred during the ‘Consolidation’ period, where Doom’s behaviour varied enormously without the excuse of humour or being aimed exclusively at young children. Ironically, despite his own claims that ‘I was able to keep the continuity tight’ the main source of this inconsistency was Jim Shooter and his Marvel Superheroes Secret Wars series [13].

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The fact that the unified catalogue can be used to give empirical evidence of theoretical proposals such as this periodisation is one of the key arguments in favour of using the tool for analysis. These and other findings may seem obvious to someone familiar with the texts, but by using this method they can be shown to be empirically accurate.

The Signifier Survey Another way to look at the usefulness of this form of analysis is to compare its findings with the results of the signifier survey. Using a survey to crowdsource the initial terms for the character components proved to be an excellent starting point for subsequent data entry, generating a rich source of information about what Doom’s signifiers might be, highlighting many signifiers that I would have struggled to name or might not otherwise have noticed. There were however several issues with the design of the survey. For example, it would have made more sense to respondents if questions about their own experience of Doctor Doom had come at the start of the survey. This would have given them a clearer idea about what was being asked, as well as giving them space to demonstrate their own knowledge rather than trying to do so in other sections. In addition, ‘behaviour’ was still being asked about as a single question, and the huge range of different responses which respondents gave echoed the difficulties faced with this component in the first iteration of data entry. This experience forced further thought on how to solve the issue of categorising behaviour and was a contributing factor in the decision to separate it out into its own group. In addition, although the responses to the ‘behaviour’ question were not directly used for the eventual analysis, the results were highly informative in shaping what eventually was looked for. Another issue was with the recruitment of respondents. Targeting fans and professionals from the comics industry using the Marvelagedoom twitter account resulted in a heavy bias towards consumers of comics rather than other media, especially consumers of Unbeatable Squirrel Girl . This was at least partly excusable in the case of Doctor Doom as the vast majority of his appearances were in comics texts, but still some effort should have been made to access non-comics fans who might have been aware of him from other sources, and this should definitely be pursued more actively for any future research on other characters. The final major issue was with the large number of replies received. The high response rate was largely due to the popularity of Ryan North’s twitter account and the enthusiasm of his followers, and this was also the cause of the bias towards Unbeatable Squirrel Girl , which North was writing at the time. As stated in the survey analysis, while too much data was quite a pleasant problem to have, it became clear during data cleaning and analysis that fewer responses would have achieved the same results much more quickly.

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Despite these issues, the survey was a great help in the development and application of the unified catalogue of transmedia character components and, with the caveats above, I would recommend its use for any future use of this overall methodology. Comparison with Main Analysis Although the survey was intended to be a guide for later data entry and analysis rather than an accurate assessment of global fan opinion, it does still offer a sizeable sample of opinions about Doom by people who have a wide knowledge of the character across different media, with the vast majority of the 225 respondents having knowledge of Doom’s appearances in at least three different media types. With this in mind, we can compare the overall results of the ‘Signifier Survey Conclusion’ section to the results gathered by the unified catalogue of transmedia character components, thus demonstrating the sort of additional information that the catalogue can uncover. He is an arrogant, megalomaniacal, egotistical genius who is obsessed with Reed Richards, the welfare of his country, and the fate of his dead mother. After data was recoded the catalogue showed that the most popular descriptions of Doom were ‘Evil’, ‘Dangerous’, ‘Genius’ and ‘Tyrant’. ‘Egomaniac’ and ‘Megalomaniac’ also appeared in the top 20 descriptions, showing that survey respondents gave an accurate reflection of his perceived behaviour. The BFI analysis also showed him to be disagreeable and always looking for new experiences (i.e. new ways to conquer the world) which also complied with the survey’s view of him. However, the two differed on Doom’s motivations. The main analysis showed that he was driven by a need to prove his own superiority in general, rather than specifically over Reed Richards. When Doom appeared in The Fantastic Four comic series Reed Richards was usually the target of his quest for superiority, but in other texts, and especially in other media, this was not the case. Indeed, there were several versions of Doctor Doom that did not include Reed Richards at all, such as the Amazing Spider-Man newspaper strip or Spider-Man cartoon, where even the origin story was retold without any mentioned of his supposed nemesis. Doom’s rule of Latveria did appear in the analysis as a significant motivating factor, although this was more often mixed in with his desire for power and control rather than necessarily being driven by genuine concern for his subjects. The obsession with his mother, meanwhile, did not appear at all within the sample, and only very rarely in the overall corpus. Her death was mentioned in Doom’s original origin story, but his annual attempt to free her from hell was not introduced until Astonishing Tales #8 [14], some ten years after his first appearance. One reason for this disparity could be that the survey respondents were discussing their own knowledge of Doom up until the present day, not

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just during ‘The Marvel Age’. His obsession with his mother was certainly emphasised more after this period ended, beginning with the graphic novel Doctor Strange and Doctor Doom: Triumph and Torment [15] which itself was mentioned by 5% of respondents, and this obsession has also been retrospectively given as motivation for his ongoing quest for power [16]. Doom’s obsession with Reed Richards has also been remarked upon as a prime motivational force, notably by Mark Waid who described Doom as someone who ‘would tear the head off a new-born baby and eat it like an apple while his mother watched if it would somehow prove he were smarter than Reed’ [17] However, if this particular motivation was really so central to Doom’s character then one would expect it to be more present in these earlier texts, where he was spending more time interacting with The Fantastic Four, yet it is not. This is shown by his use of dramatic actions, often involving his hands, and penchant for striking dramatic poses, as well as the way he refers to himself in the third person, making self-aggrandising claims and using phrases such as ‘Fools!’, ‘Bah!’ and ‘Curse you!’. The main data analysis shows that Doom struck dramatic poses in 62.9% of all texts where he had physical actions, made specific hand gestures in 46.77% and shot bolts of energy from his hands in 43.55%, showing that the survey assessment is accurate. However, his propensity to loom menacingly over other characters was not mentioned at all by respondents, despite this happening in over half of the texts, and only two respondents mentioned his use of viewing screens, which happened in 40.32% of texts. The main analysis also showed that Doom’s physicality was based around power, distance, and inventiveness, and while power was clearly mentioned by the survey, with his use of machinery also being referred to several times, there was little awareness of the fact that he tended to do this at a distance, using viewing or listening devices to communicate and robots to do his dirty work. ‘Doombots’ were mentioned elsewhere, but viewing screens were not, which may be because ‘viewing screens’ have become part of daily life in the twenty-first century. If this explanation is accepted then it makes the case even more strongly for an empirical method of assessing character components which is not swayed by personal experience or changing times. The survey respondents’ assessment of Doom’s speech was much closer to what was uncovered in the main analysis, with his habit of speaking in the third person and general self-aggrandisement, as well as a formal tone and other references to himself as ‘Doom’, appearing in the vast majority of texts. The top two specific phrases used were ‘Fools!’ and ‘Bah!’ while others such as ‘You dare?’ and ‘Dolt!’ were also identified by respondents. The only phrases that did not occur in the sample were ‘Curse you!’ and those found in memes, such as ‘Curse these confounded squirrels!’ and ‘Doom toots as he pleases!’ demonstrating that respondents had a very clear idea of how Doctor Doom spoke.

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He is generally referred to in three ways—as Doctor Doom (or simply ‘Doom’, usually by himself), variations of his full name Victor von Doom, or with honorifics referring to his status as the ruler of Latveria. He wears a mask and suit of armour with a green tunic over the top, a hooded cloak, attached with golden clasps, and a leather belt with a gun holster. His eyes are visible beneath the mask, and his face is scarred after an accident. Both of these assessments of Doom matched the analysis, reflecting the fact that Doom was consistent in the categories of ‘appearance’ and ‘names and titles’ throughout the sample and across time and media. The accident which scarred his face occurred while at University with Reed Richards, and is the most important event in his life, followed by the damnation of his mother. Doom’s accident was referred to during the sample, and also throughout the corpus, but not as often as one might have expected from the survey. During ‘The Marvel Age’ his takeover of Latveria was mentioned more often than the accident and his days at University with Reed Richards, while the fate of his mother was barely mentioned at all. In fact, his accident was referred to in the sample the same numbers of times as the events of Secret Wars, despite these events occurring only in comics and only during the Consolidation period. This was due to the fact that Doom was a lead character in that series, and was often used as part of a recap of those events, as shown in Fig. 6.2. In these cases we might say that Doctor Doom was used as a signifier of Secret Wars, rather than Secret Wars being used as a signifier of Doom. It is important to note here that a previous event, or any other signifier, may still be important to the understanding of a character, even if it is not often directly mentioned in a text. One could well argue that Doom’s relationship to Reed Richards was still the most important aspect of his character even though the vast majority of texts made no reference to it. Similarly, some texts will have a much greater impact on readers than others so that, for instance, the events recounted in the much-reprinted Fantastic Four Annual #2 [19] could be said to carry more weight in the general perception of Doom than the tiny single panel cameo shown in Fig. 6.2. These sort of assessments of what ‘matters’ for a character are the realm of literary studies and close readings— this empirical model offers an alternative viewpoint which, while providing much new information, does not claim to cover everything. The characters that Doom is most often associated with are the Fantastic Four (especially Reed Richards) and his own Doombots. Other close associates include Namor the Sub-Mariner, his adopted son Kristoff, and his manservant Boris, but he interacts with a wide variety of characters across the Marvel Universe. The main analysis showed that Doom did associate more with the Fantastic Four than other characters, although it was The Thing rather than Reed Richards with whom he made most appearances during this period, across

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Fig. 6.2 Doom (bottom right) as part of a Secret Wars recap in The Spectacular Spider-Man #111 [18]

both comics and non-comics. Captain America and various Latverian lackeys appeared more often than Namor, although the former was present in 18.84% of texts, as was Iron Man. Boris was present in five texts in the sample while Kristoff was not in any, although he did make three appearances in the full corpus. The respondents’ belief that Kristoff appeared more often than he did in the sample is partly due to the fact that they were answering about Doom overall, not just for this period, with Kristoff becoming a regular character in Doom’s stories shortly after the end of ‘The Marvel Age’. The relatively few appearances by Boris may be due to the fact that Doom’s own supporting cast were less likely to appear in stories where he was himself a supporting character, and so would not be in as many texts as characters such as Captain America and Iron Man whose series Doom would guest star in. Aside from that, the main data analysis agreed with the survey assessment that Doom did interact with a wide variety of characters across the storyworld. He can mostly be found in Latveria, especially in his castle, or in New York locations such as the Baxter Building, his castle in the Adirondacks, or the Latverian Embassy. The top three locations in the main analysis were New York, Latveria, and Castle Doom, with The Baxter Building in sixth place. The castle outside New

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York and the Latverian Embassy did appear in the sample as well as the wider corpus, but not as often as more general locations such as laboratories, control rooms and dungeons. The most noticeable setting which appeared in more than 10% of texts but was not mentioned by the survey was the United Nations Building, which was in 7 of the 69 sampled texts. Otherwise the respondents’ assessment of Doom’s settings was similar to the results of the main analysis. He was created by Stan Lee and Jack Kirby, with other notable creators being John Byrne, Jonathan Hickman, Mark Waid and Mike Weiringo, and Walt Simonson. Three of the names here do not appear in the corpus due to the time period. Mark Waid’s first professional comics work was published by DC in 1985, and he would not work for Marvel until the mid-1990s, so would not have had any texts within this corpus, while Jonathan Hickman and Mike Weiringo did not start their professional comics careers until after ‘The Marvel Age’ had ended. Walt Simonson did illustrate Thor #271 [20] which featured a one-panel cameo by Doctor Doom, but he would not regularly work on the character until his run on Fantastic Four began in 1989—again, outside of this period. Stan Lee, Jack Kirby , and John Byrne did appear in the sample, with Lee and Kirby the second and fourth most credited authors respectively, although Byrne was only credited in 5 of the 69 texts of the sample. The author credited on more texts than any other is Jim Shooter, who was credited in 42% of texts but only mentioned by 9% of respondents. The top ten textual authors in the data analysis included several who were not mentioned in the survey at all. These were letterers, in the case of Joe Rosen, Artie Simek and Tom Orzechowski, or the colourist Glynis Wein. Their names appeared throughout the sample, credited in the same place in (usually) the same way as other textual authors, yet they do not appear to have entered the consciousness of comics fans in the same way. As with previous events, it could well be argued that a letterer or colourist did not have the same input into a story as a writer, artist or inker and therefore did not have the same impact on fan perceptions, but they were still present in the texts as textual authors and so should be considered as such. This reiterates one of the advantages of this form of analysis, in that it steps aside from preconceived ideas of the signifiers of a character to allow an empirical, unbiased, view of what is actually contained within the texts. He is a Marvel comics character. This is undoubtedly true! In general, the survey and main data analysis tended to agree on the aspects of Doom which were available within the texts themselves, such as his appearance, other characters , and dialogue. Where they tended to disagree was in the components that required some interpretation of meaning, such as his motivations and important past events. The data analysis was based entirely on information found within the text, rather than any extrapolations, with motivations usually taken from those expressed by Doom himself and previous

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events being a record of those referred to within the text, so these difference may be due to the types of motivation or previous events that had particular resonance for respondents. Similarly, the textual authors remembered by readers were heavily focused on those who were judged to have been ‘important’—the writers and artists— rather than the letterers, inkers, colourists, and others who also contributed to the texts. This is an understandable position, and is one which I would also have had before beginning this research, but it is one based on personal opinion rather than empirical facts. This is especially true in the case of inkers, where well-known inkers such as Joe Sinnott or Bob Layton were deemed important enough by readers to appear in their list of creators, but others such as Mike Esposito were not. None of this is to say that the survey respondents were ‘wrong’, or that the importance of signifiers should only be assessed through their number of mentions within texts. This is simply a different, more empirical, way of examining comics texts which offers different insights to traditional methods. To summarise this comparison, a revised version of the characteristics of Doctor Doom, based on the main data analysis rather than the survey, would read as follows: • He wears a metal mask with square holes through which his eyes can be seen, a triangular nose, and a grill for a mouth. This is part of a metal suit of armour over which he wears a green cape, tunic and hood fastened with gold clasps, and a leather belt. • He is generally referred to as ‘Doctor Doom’, ‘Doom’, variations of his full name ‘Victor von Doom’, or with honorifics referring to his status as the ruler of Latveria. Occasionally familiar names such as ‘Doomsie’ and ‘Doc Doom’ are used by other characters wishing to register their disrespect. He refers to himself in the third person, making self-aggrandising claims and speaks in a formal tone. He often insults others using phrases such as ‘Fools!’, ‘Bah!’ and ‘Dolt!’. • He favours dramatic poses and gestures which demonstrate his power over others, especially gestures involving his hands. He prefers to exercise this power from a distance via ‘Kirbytech’ machinery, particularly viewing screens, high-tech weaponry, and Doombots or other robots. • The characters that Doom is most often associated with are the Fantastic Four (especially The Thing), the Latverian people, the Avengers and, particularly in non-comics texts, Spider-Man. He interacts with a wide variety of characters across the Marvel Universe. • He can mostly be found in Latveria, especially in his castle, or in New York locations such as the Baxter Building, Avengers Mansion or the United Nations building. He also exists in space and other dimensions, notably Battleworld. He can be found in laboratories, control rooms, aircraft, or dungeons.

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• The most recalled incident in his past is his takeover of Latveria, although this is never fully described. Various aspects of his origin, including the accident that scarred his face, and his encounters with the Fantastic Four, are important to his history, as is his time on Battleworld. • He is described as villainous and powerful, and though negative descriptions such as ‘evil’ and ‘dangerous’ are often used, he is also called ‘awesome’ and ‘good’. • He is disagreeable to other people, but conscientious in his planning and open to new experiences. His emotional stability varies in comics, but in other media he tends towards emotional stability rather than neuroticism. • He is motivated by a need to prove his own superiority over others and a quest for power, locally in Latveria but also globally, as he believes the world would be a better place under his rule. He is also driven by intellectual curiosity and a hatred of the Fantastic Four. • He was created by Stan Lee and Jack Kirby, with other notable creators being Jim Shooter, Joe Rosen, Artie Simek and Joe Sinnott. • He is a Marvel comics character. The fact that such a characterisation of Doom could be generated through the use of the unified catalogue of transmedia character components, and backed up by empirical evidence, is proof that is it a useful analytics tool for examining this particular character, and the fact that it worked equally well on the two characters called ‘Dennis The Menace’ shows that it has a use for other characters too.

Conclusion This final section will examine the usefulness of both the tool and the case studies chosen, before making some suggestions about possible future work in this area. Does the Catalogue Work? These analyses have demonstrated the usefulness of a unified catalogue of transmedia character components. Bringing together existing theories of transmedia into a single catalogue like this provided a straightforward, easyto-understand description of all the different character components described by the source theories. It also provided a clear, practical, way to describe the ‘behaviour’ of fictional characters, something not otherwise supplied, and grouped all of these components into four logical groups to further aid understanding. Doing this generated a practical tool for research rather than simply suggesting aspects which other researchers might wish to think about, or

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putting forward a way to describe a particular character. It provided a practical toolkit of methodologies which can be used for describing any fictional character across time and media. Using the tool in this way not only provided empirical evidence for assessments which might otherwise have been based on conjecture, but also uncovered character signifiers that would not necessarily have been revealed using more conventional humanities-based analyses. This was particularly shown in the analysis of the British Dennis The Menace, where using the catalogue allowed me to uncover new insights into the character’s fictional personality even after over 45 years of experience of his stories. This was also demonstrated in the comparison with the survey of fan perceptions of Doctor Doom, where the survey responses often missed aspects of the storyworld that the methodology identified as regularly appearing. In many ways it was the richness of the data generated which most demonstrated the usefulness of this tool. The analyses presented in this book only scratch the surface of what could be done with more in-depth statistical methods. One obvious next step would be to use correlation to examine the strength of relationships between different results [21] For example, this statistical method would be able to show whether there was any relationship between Reed Richards appearing in a story and Doctor Doom’s behaviour becoming less agreeable, or between his perceived behaviours and the textual authors responsible for creating the particular texts. There is a wealth of possibilities for this and other types of analysis, and the hope that somebody will be sufficiently interested to investigate further is one of the main motivations for making all of this data publicly available. Another powerful outcome of this tool being used more widely would be the ability to allow comparison of different studies. As has already been seen, repeating the methodology on the two different versions of Dennis The Menace not only allowed for them to be directly compared to each other, but also for comparisons to be drawn between them and Doctor Doom, notably in the relative coherence of each character’s behaviour over time according to the BFI scale. If another study used the same methodology then the resulting dataset could be directly compared to, and analysed alongside, these and any others derived in the same way. The data generated in this book was analysed in a simplistic way by comparing the results within individual dimensions, but there is scope for much more work to be done, especially if the tool was used for larger surveys gathering more information. All manner of analyses could be carried out by bringing together larger datasets of linked studies that could generate a wealth of information about the nature of characters and their storyworlds. This sort of data comparison is already commonplace in other fields. To take an example from my own experience, The National Evaluation of Sure Start used many of the same assessment tools as the Millennium Cohort Study, which followed 19,000 children born in the year 2000 at similar intervals [22, 23] This meant that the two different cohorts could be directly compared,

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with the results of the Millennium Cohort Study used as a control group to identify differences in those who had and had not been exposed to the Sure Start programme [22] This saved enormous amounts of effort, time and money, as the earlier data could be reliably re-used rather than having to be collected again. In the same way, if the field of Comics Studies was able to formulate its own empirical tools which were then used for multiple studies, it would allow these datasets themselves to be reliably cross-analysed, creating the possibility for greater collaboration and the uncovering of information which would not be possible for individual researchers working alone. Another personal example comes from the field of psychiatric research, where using universal tools such as the Mini-Mental State Examination means that such instruments can be easily re-used with confidence and the results compared to those of other studies [24] The existence of such tools also means that researchers do not need to devise research methods from scratch, and can instead move straight to data collection and analysis safe in the knowledge that the tools they are using have been widely tested and verified. As above, the results of any analysis of this dataset could then be directly compared to the results of the analysis using the same methodology. A universal system for character analysis would thus not only provide huge amounts of usable data but could open the way to greater insights into the development of transmedia characters across all media and time periods. Using Doctor Doom This work has hopefully shown that Doctor Doom worked well as a case study used to generate the catalogue. A wider question is whether he can be used more generally as a case study for other transmedia analyses, and for this to be the case he first needs to be conclusively identified as a transmedia character. According to Henry Jenkins’ definition of transmedia he cannot. The Doctor Doom we see in Fantastic Four comics does not live in the same storyworld as the one in The Amazing Spider-Man newspaper strip, or the Spider-Man and His Amazing Friends cartoon, or any of the other noncomics media since then. These are all versions of the same character, but their storyworlds are not the same. However, I believe that the idea of ‘Transmedia Character Networks’, where different versions of the same character exist across different media without necessarily sharing a storyworld, is a more practical way to view modern transmediality. This can especially be seen within superhero storytelling, with the two major superhero transmedia storyworlds of the ‘Marvel Cinematic Universe’ and ‘DC Extended Universe’ embracing this approach, not just as a way of producing transmedia outputs but as a storytelling tool in its own right. Several versions of the same character may exist in different media at the same time, with the expectation that audiences will be sufficiently aware of transmedia expectations to differentiate between them. Going

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further, different versions of the same characters can even interact with each other in movies such as Spider-Man: Into The Spiderverse [25] and SpiderMan: No Way Home [26], and TV shows such as Loki [27] The Flash [28] and Supergirl [29]. The co-existence of different versions of the same characters is not a new idea. The term ‘The Multiverse’ was first used in a fictional context by Michael Moorcock in 1963, two years after the story ‘The Flash of Two Worlds!’ had shown two versions of the same character, from two separate versions of Earth, meeting in The Flash #123 [30], while the idea of parallel worlds was first proposed as far back as the Atomists of ancient Greece [31, 32] Even before this, while early transmedia characters did not ‘crossover’ with different versions of themselves in this way they did exist simultaneously as the same character operating across different storyworlds. Wandering characters such as Donald Duck could exist within different storyworlds in different media, such as cartoons, comic strips or newspaper strips, but still be recognised and accepted by audiences as the same character [32]. These characters carried their own storyworld with them, including recurring supporting characters, settings, objects and so on, rather than necessarily inheriting them from each diegesis they entered [7]. The results of the data analysis show that Doctor Doom very much fits into this category. His coherence across different media and over time has been clearly demonstrated, not just in terms of his character components and behaviour, which could be termed as ‘belonging’ to the character themselves, but also for components which relate to his storyworld. Other characters, objects , and settings remain coherent wherever he appears, almost like a bubble of ‘Doctor Doom-ness’ which he carries around with him.2 Thus, if we use these wider definitions of early transmedia characters operating as transmedia global character networks, rather than Henry Jenkins’ original definition, Doctor Doom can definitely be said to be a transmedia character. However, although he served as a useful case study for assessing the unified catalogue of transmedia character components, accumulating a rich dataset, this does not necessarily mean he would be similarly useful in other research. One major reason for this was that, during this period at least, Doom did not appear in a particularly large number of non-comics texts. This caused problems when the sampling methodology was applied, as it left an even smaller number of non-comics texts to analyse, but a solution to this would be to include a larger number of these sort of texts in the sample and adjust the results to ensure representativeness. Another drawback to using Doctor Doom as a case study was that there was very little academic writing specifically related to him. The almost complete absence of traditional journal articles or book chapters specifically about Doom meant that I had to widen my reading to include more general non-academic 2

After five years of trying I have still not found a better way of saying this.

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texts concerning super-villains. Here, one of the most valuable texts for understanding the nature of Doom turned out to be Stan Lee’s decidedly non-academic text pieces in Bring On The Bad Guys, which combined Lee’s entertaining prose style with some surprisingly thoughtful insight into what made the character work [33] By contrast, the advantage of this was that there were fewer preconceived ideas about Doctor Doom. As the survey showed, fans definitely had their own thoughts about what made Doctor Doom who he was, and the comparison with the main data analysis showed that these opinions were, for the most part, backed up by the texts, but unlike Batman there was not a pre-existing wealth of material stating what the character ‘means’. Similarly, there was very little academic analysis of either version of Dennis The Menace, although ironically a major text on the American version is forthcoming from Henry Jenkins himself [34] As was discussed in ‘A Tale of Two Menaces’, this meant that there was little pre-existing opinion on the two to use as a starting point, but in common with Doctor Doom this did not prevent a wealth of data being accrued using the catalogue. In both examples it was clear that the tool could be used to generate a wealth of data that could not only withstand interrogation but could also become a jumping off point for further investigation and new theories. Perhaps most excitingly, it showed that using the same methodology in this way enabled comparisons to be drawn between entirely different characters operating in very different storyworlds at different points in time. If sufficient further studies were undertaken in this way, and the datasets were made available, then all manner of analyses could be undertaken, not just into individual characters but more globally about the nature of characters themselves. Donald Goes Dutch With this partly in mind, the next step for my own research is to run a project, in conjunction with Dr Ian Horton, using the unified catalogue of transmedia character components to examine Donald Duck as a transnational character. Donald Duck first appeared in 1934 in the Disney animation Wise Little Hen which was adapted as part of the Silly Symphonies Sunday newspaper strip in the same year [35, 36]. The first European Disney comics were published in the early 1930s, usually featuring translated reprints of these strips, but the real boom in European Disney comics began after World War II, when various series were launched across Europe [37]. Initially these featured translations of American strips with new locally produced covers, but by the 1950s the European publishers had begun to commission new strips as well. During this period the publishers began to share strips with each other, so as well as reprinting American works and including material produced locally they would also reprint some of the stories being created in other European countries. These would then need to be translated themselves, and sometimes redrawn

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to fit the local context. For example, a local currency drawn on money bags might be redrawn for different countries. This project intends to examine the coherence of the character of Donald Duck through these different iterations using the unified catalogue of transmedia character components to examine transnational differences rather than transmedia ones. It will concentrate on comparisons between strips from the Netherlands and the USA, but will draw upon the expertise of comics scholars and fans across Europe to build a database of information on a sample selected using random stratified sampling. Data for this sampling will be taken from INDUCKS, a fan-created online database that aims to record the content of every Disney comic ever published. Although fan-created and owned, INDUCKS is now the official archive used and updated by European editors and publishers of Donald Duck comics, and contains information on publications, reprints, creators and characters contained within them. The project will also engage with fans, scholars, and professionals working on current Donald Duck comics to generate a qualitative analysis of the character’s history, especially his emergence as a character that many in the Netherlands recognise as a specifically Dutch character [37]. By combining these two approaches we hope to create a new understanding of Donald Duck as a transnational character, while also testing the catalogue in new ways. By testing it out on another character, and particularly by using it to examine coherence across national borders rather than different media, we also hope to show that the tool itself can be used in many different ways. Other Possibilities Beyond this the possibilities for further studies are practically limitless. One use might be to employ the tool in gathering data for an examination of how characters change when adapted transnationally. For example, the successful BBC series Ghosts has recently been adapted into an equally successful version in America, with some characters remaining almost the same and others being changed or dropped. The catalogue could be used to compare exactly how such characters differ from one version to the other, providing empirical evidence for a discussion of cultural differences between the two markets. Similar analyses could be carried out for other British series such as The Office or Steptoe and Son, or for transfers between other countries and, again, if sufficient studies were done the results could be compared to glean further insight into the nature of such adaptations. The tool could be used to examine the nature of specific characters. The main case study in this book produced a deeper understanding of Doctor Doom, but it could also be used to analyse characters who have made many more appearances across different media. An excellent example of this would be Sherlock Holmes—there are literally hundreds of different versions of this character across practically all different types of media, so the catalogue could

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be used to assess what exactly is the essence of ‘Sherlock Holmes-ness’ [38]. Audiences recognise all of these characters as Sherlock Holmes, but the tool could be used to find out why this is. It might be that simply the name ‘Sherlock Holmes’ is sufficient. The assessment of other characters in this book has shown names and titles to be an important identifier, but related names—such as ‘Sherlock Hound’ in the Italian animated series—could equally designate that a character belongs to the ‘Sherlock Holmes’ group [39]. In addition, there are cases where a shared name does not necessarily mean the character is the same. This has already been shown with the name ‘Dennis The Menace’, but it is also the case with so-called ‘Legacy characters’ in superhero stories. Over the past twenty years comics companies have sometimes attempted to update their characters by changing the ‘secret identity’ of well-known superheroes. For example, the original Captain America, Steve Rogers, passed the title on to his friend Sam Wilson, previously The Falcon, in 2014, and this storyline was referenced in Avengers Endgame and the TV series The Falcon and the Winter Soldier. This change was controversial among fans in both comics and TV series, with some claiming this was an act of ‘political correctness’, replacing a traditionally white character with a black character, and similar issues arose around Miles Morales taking the name ‘Spider-man’, or Carol Danvers as ‘Captain Marvel’. Some of this discourse was self-evidently racist or sexist, with fans claiming that these changes were ‘forced’ upon them, but the tool could be used to quantitatively analyse how these changes actually worked. Did changing the secret identity of a character create an entirely new character, for instance, or did other components remain the same? If that was the case, did other components rank more highly than Names and Titles in stating that this was the ‘true’ version? And did all such different versions exist together in the same transmedia character network? Some of these questions generate highly emotive responses from people with longstanding attachment to the characters, so the tool might be useful as a way to investigate all of these issues in a more data-led way. Another practical use could be to assist in identifying uncredited textual authors, especially writers. In the Doctor Doom sample, for example, comics texts almost always named writers and artists, but this was not necessarily the case for other characters or in other periods, as was shown by the Dennis The Menace analysis. Although it is often possible to identify the artists for some stories, it is not as straightforward to identify writers so easily, and so the catalogue might be able to help by identifying specific combinations of attributes that identify specific writers. It might be possible to use a combination of a character’s dialogue, motivations, and associated characters, for example, to generate a ‘fingerprint’ for specific writers that could then be used to pick out uncredited stories. These are just some examples of how the tool could be used. I hope others will come up with their own questions and use the catalogue to answer them, and in order to encourage this the appendices that follow will include

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a summary of how the tool works and a straightforward guide on how to use it. As has been shown, using the same methodology for separate analyses means that there results can be easily compared, and it is my great hope that others will not only use this methodology but also share their datasets, so that over time much larger analyses can be undertaken. I am also aware that such further uses may uncover issues with the methodology itself, perhaps even entire dimensions that have been missed. In all circumstances I welcome correspondence with other researchers, and hope that this catalogue can be of use to them.

References 1. Pearson, Roberta, and William Uricchio. 1991. The Many Lives of the Batman: Critical Approaches to a Superhero and His Media. London: Routledge. 2. Klastrup, Lisbeth, and S Tosca. 2004. “Transmedial Worlds—Rethinking Cyberworld Design.” 3rd International Conference on Cyberworlds. Tokyo: IEEE Computer Society. 3. Bertetti, Paolo. 2014. “Toward a Typology of Transmedia Characters.” International Journal of Communication 2344–2361. 4. Freeman, Matthew. 2016. Historicising Transmedia Storytelling. London: Routledge. 5. Thon, Jan-Noël. 2019. “Transmedia Characters: Theory and Analysis.” Frontiers of Narrative Studies 5 (2): 176–199. 6. Jenkins, Henry. 2008. Convergence Culture: Where Old and New Media Collide. New York: New York University Press. 7. Scolari, Carlos, Paolo Bertetti, and Matthew Freeman. 2014. Transmedia Archaeology: Storytelling in the Borderlines of Science Fiction, Comics and Pulp Magazines. Basingstoke: Palgrave Macmillan UK. 8. Wikimedia Foundation. 2021. “Doctor Doom in Other Media.” Wikipedia. 15 August. https://en.wikipedia.org/wiki/Doctor_Doom_in_other_media. 9. IMDb.com. 2021. IMDb. 15 August. https://www.imdb.com/. 10. Wolfman, Marv. 1979. Doomsday. New York: Pocket Books. 11. Mullins, Craig S. 2021. Data Portability. February. https://searchcloudcomp uting.techtarget.com/definition/data-portability. 12. Hibbett, Mark. 2021. “Doctor Doom in the Marvel Age: Dataset.” University of the Arts London, 24 September. https://doi.org/10.25441/arts.16676830.v1. 13. Shooter, Jim. 2011. Secrets of the Secret Wars. 4 April. Accessed January 2018. http://jimshooter.com/2011/04/secrets-of-secret-wars.html/. 14. Conway, Gerry, Gene Colan, and Tom Palmer. 1971. “...Though Some Call It Magic!” Astonishing Tales (Marvel Comics) 1 (8). 15. Stern, Roger, Mike Mignola, and Mark Badger. 1989. “Doctor Strange and Doctor Doom: Triumph and Torment.” Marvel Graphic Novel (Marvel Comics) 1 (49). 16. Wolk, Douglas. 2021. “The Voice of Latveria.” 19: This Man... This Demon! (with Laura Hudson). 29 June. https://voiceoflatveria.com/2021/06/29/19this-man-this-demon-with-laura-hudson/.

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17. Smith, Andrew. 2013. “Supervillains Who Need Superheroes (Are the Luckiest Villains in the World).” In What is A Superhero? by Robin S Rosenberg and Peter Coogan, 101–106. New York: Oxford University Press. 18. Owsley, Jim, Rich Buckler, and M Hands. 1986. “And Then the Gods Cried.” The Spectacular Spider-Man (Marvel Comics) 1 (111). 19. Lee, Stan, Jack Kirby, and Chic Stone. 1964. “The Fantastic Origin of Doctor Doom!” Fantastic Four Annual (Marvel Comics) 1 (2). 20. Wein, Len, Walt Simonson, and Tony DeZuniga. 1978. “...Like a Diamond in the Sky!” Thor (Marvel Comics) 1 (271). 21. Walker, Ian. 2010. Research Methods and Statistics. Basingstoke: Palgrave Macmillan. 22. Melhuish, Edward, Jay Belsky, and Jacqueline Barnes. 2010. “Sure Start and its Evaluation in England.” In Encyclopedia on Early Childhood Development, by RE Tremblay, RG Barr, RDeV Peters and M Boivin, 1–7. Montreal, Quebec: Centre of Excellence for Early Childhood Development. 23. Connelly, Roxanne, and Lucinda Platt. 2014. “Cohort Profile: UK Millennium Cohort Study (MCS).” International Journal of Epidemiology 43 (6): 1719–1725. https://doi.org/10.1093/ije/dyu001. 24. Lindesay, James, Carol Jagger, Mark Hibbett, Susan M. Peet & Farida Moledina (1997) “Knowledge, uptake and availability of health and social services among Asian Gujarati and white elderly persons.” Ethnicity & Health 2 (1): 59–69. 25. 2018. Spider-Man: Into the Spider-Verse. Directed by Bob Persichetti, Peter Ramsey and Rodney Rothman. Produced by Sony Pictures Animation. 26. 2021. Spider-Man: No Way Home. Directed by Jon Watts. Produced by Colombia Pictures. 27. 2021–2023. Loki. Produced by Marvel Studios. 28. 2014–2023. The Flash. Produced by Warner Bros. Television. 29. 2015–2021. Supergirl. Television. Produced by Warner Bros. Television. 30. Fox, Gardner, Carmine Infantino, and Joe Giella. 1961. “The Flash of Two Worlds!” The Flash (DC Comics) 2 (123). 31. Mann, George. 2001. The Mammoth Encyclopedia of Science Fiction. London: Robinson. 32. Levene, Lesley. 2010. I Think, Therefore I Am. London: Michael O’Mara Books. 33. Packard, Stephan. 2019. “Which Donald is This? Which Tyche is This? A Semiotic Approach to Nomadic Cartoonish Characters.” Frontiers of Narrative Studies 5 (2): 248–267. 34. Lee, Stan. 1976. Bring on the Bad Guys: Origins of Marvel Villains. New York: Simon & Schuster. 35. Jenkins, Henry. 2023. Where the Wild Things Were: American Childhood and the Permissive Imagination (in preparation). University of Southern California. 36. INDUCKS. 2023. The Wise Little Hen. Accessed March 23, 2023. https://ind ucks.org/story.php?c=ZS+3403. 37. Bryan, Peter Cullen. 2021. Creation, Translation, and Adaptation in Donald Duck Comics. The Dream of Three Lifetimes. Cham: Palgrave Macmillan. 38. Rutigliano, Olivia. 2021. The 100 Best, Worst, and Strangest Sherlock Holmes Portrayals of All-Time, Ranked. Accessed March 23, 2023. https://crimereads. com/100-sherlock-holmes-ranked/. 39. Kunz, Tobias and Lukas RA Wilde. 2023. Transmedia Character Studies. New York: Routledge, p. 157.

Appendix A: Using the Unified Model of Transmedia Character Coherence

The unified catalogue of transmedia character components is a tool for measuring aspects of a characters so that they can be tracked across time, media, nationalities or other borders. It contains thirteen dimensions grouped together into those related to the character themselves, their storyworld, their behaviour within that storyworld, and the authors who generate their texts.

Preparation The catalogue can be used as a way to assess a single appearance of a character in a specific text, but it is designed to be used as a way to gather together a dataset across a sample of texts. This sample can be put together in various ways—stratified random sampling has been used in the main case study here in order to generate a sample that is representative of the corpus as a whole, but other statistical methodologies are available. Once a sample has been identified the next step is to generate an initial list of components for each dimension. This list is created as a starting point for data entry rather than a definitive guide. This list can be built in various ways, including an analysis of current literature on the subject or, if this is not available, a survey of other individuals—fans, academics or others—with knowledge of the character. An example of a survey that could be used for these purposes is included in Appendix C.

Data Collection With the starting points identified collection of data from individual texts can begin. This can be done in different ways, such as analysing each text in terms of all thirteen dimensions, or going through the entire sample looking for one dimension at a time, or a mixture of the two. The dimensions to be analysed are described below. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 M. Hibbett, Data and Doctor Doom, Palgrave Studies in Comics and Graphic Novels, https://doi.org/10.1007/978-3-031-45173-7

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Character-Specific Components These dimensions are concerned specifically with the character themselves. Appearance The visual appearance of the character, including what they personally look like and what they wear. Names and Titles The names and titles used to refer to the character by themselves, other characters, narratorial voices, or other aspects of the text such as story titles. Physical Actions Physical actions particular to the character which occur within the text itself. Dialogue Frequently used phrases or manners of speaking which are associated with the character and appear regularly in their own speech or thoughts.

Storyworld-Specific Components These dimensions deal with the overall storyworld that the character exists in. Larger storyworlds may include many components which are nothing to do with the character being examined, as they occur in different stories or parts of the world, and so data entry should record whether or not the character interacts with them should be recorded as part of data entry. Similarly, some texts may contain multiple stories, and so data entry should only record information for the stories in which they appear, even if this appearance is very brief. Locations All of the settings which appear in the story, including both time and space. Other Characters Other characters who appear in the same story as the character under investigation. Objects Physical items which appear in the text and act as part of the plot which are not otherwise categorised as part of locations. Previous Events All previous events which are directly mentioned within the story, whether they have actually been seen in other texts or are new inventions.

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Behavioural Components These dimensions describe the way that the character interacts with the other aspects of their storyworld, whether that be other characters, locations, objects or the world’s history. Perceived Behaviour How the character is perceived by themselves, other characters, narratorial voices, or other aspects of the text. This dimension is gathered by recording all of the descriptive words applied to the character within the text. Personality Traits How the character’s personality is perceived by the consumer of the text, gathered by using some form of the Big Five Inventory. Motivations What drives the character’s overall behaviour. Motivations are often expressed out loud or in recorded thought, especially within the superhero genre and even more so for super-villains.

Authorship Components This group is about the people or organisations that are noted as the creators of the texts within the texts themselves. It is important to note that although, unlike other dimensions, these are concerned with the non-fictional rather than fictional world, the information should still be taken from the texts rather from any external sources. Market Authors These are the authors or organisations who are named within the texts as a way to guide the reader, listener or viewer’s expectations of what they will find within it, even if they do not have anything to do with the actual creation of this specific text. Textual Authors These are the named authors or organisation who are credited as working on the creation of the text, listed as part of the text itself. It is recommended that data is entered directly into a computer system, whether that be a database system or simple spreadsheet. To enter data, the researcher is advised to read through the text and note down each of the components that appear for each dimension. For example, when looking for components for the ‘Appearance’ component one would read the text and note down every aspect of the character’s visual appearance that can be seen. It may be necessary to clean this data during the course of data entry. For

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instance, if phrases such as ‘shoes’, ‘boots’, and ‘footwear’ were being entered it might save time to recode all such items as simply ‘footwear’. However, researchers should be careful to be aware of whether different types of footwear occur—‘sandals’ and ‘wellington boots’ are very different kinds of footwear, so caution should be used. The survey can be a very helpful guide in such situations. If respondents simply mentioned ‘shoes’ in this example then it is likely (though not certain) that the type of shoe does not matter. If they did mention different types or brands, then they certainly do matter. Once all data has been entered it should be cleaned again before analysis begins.

Analysis Any reasonable data analysis methodology can be used on datasets derived using this methodology. Within this book very simple analyses have been carried out, mostly involving simple counts and rankings of results, with some use of Mean Absolute Deviation to measure coherence over time and media. Other more complex analyses of this dataset would be possible and indeed are invited, with both the Doctor Doom and Dennis The Menace datasets available online for other researchers to utilise.

Appendix B: Doctor Doom Corpus

Adams, Scott (w), John Romita Sr., Mark Gruenwald and Kem McNair (p), (i). “featuring Human Torch and the Thing”. Questprobe (video game) v1 #3 (Nov. 1984), Marvel Comics. Akin, Ian (a), Brian Garvey (a). “Doctor Doom”. Marvel Fanfare v1 #33 (Jul. 1987), Marvel Comics. Anon (w), John Romita Sr (a). “The Colorado Caper!”. Spider-Man and the Incredible Hulk v1 #1 (Jan. 1982), Marvel Comics. Anthony Kraft, David (w), Don Perlin (p), Bruce D. (i). “Of Ambitions and Giant Amoebas”. The Defenders v1 #65 (Nov. 1978), Marvel Comics. Austin, Terry (a). “The Terry Austin Portfolio”. Marvel Fanfare v1 #18 (Jan. 1985), Marvel Comics. Brodsky, Allyn (w), Don Heck (p), Mike Esposito (i). “Their Mission: Destroy Stark Industries!!”. Iron Man v1 #33 (Jan. 1971), Marvel Comics. Butler, Jeff (w), Bruce Nesmith (w). “Fold-Up Figures”. Marvel Superheroes Adventure Fold-Up Figures v1 #1 (Mar. 1986), Marvel Comics. Byrne, John (w), Ron Wilson (p), Frank Giacoia (i), Kevin Dzuban (i). “Aftermath!”. Marvel Two-In-One v1 #100 (Jun. 1983), Marvel Comics. Byrne, John (w), Ron Wilson (p), Hilary Barta (i). “Mindscape”. The Thing v1 #6 (Dec. 1983), Marvel Comics. Byrne, John (w), Ron Wilson (p), Joe Sinnott (i). “Doom!”. The Thing v1 #12 (Jun. 1984), Marvel Comics. (sample). Byrne, John (w), Ron Wilson (p), Joe Sinnott (i). “Lifelines”. The Thing v1 #1 (Jul. 1983), Marvel Comics. Byrne, John (w), Ron Wilson (p), Joe Sinnott (i). “Rocky Grimm Space Ranger Part III: In All The Gathering Gloom”. The Thing v1 #13 (Jul. 1984), Marvel Comics. Byrne, John (w, a) “The Eyes Have It!”. Fantastic Four v1 #237 (Dec. 1981), Marvel Comics. (sample).

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 M. Hibbett, Data and Doctor Doom, Palgrave Studies in Comics and Graphic Novels, https://doi.org/10.1007/978-3-031-45173-7

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Byrne, John (w, a). “Beginnings and Endings”. Fantastic Four v1 #244 (Jul. 1982), Marvel Comics. Byrne, John (w, a). “Choices”. Fantastic Four v1 #259 (Oct. 1983), Marvel Comics. Byrne, John (w, a). “Interlude”. Fantastic Four v1 #258 (Sep. 1983), Marvel Comics. Byrne, John (w, a). “Terror in a Tiny Town / The Challenge Of Dr. Doom!”. Fantastic Four v1 #236 (Nov. 1981), Marvel Comics. Byrne, John (w, a). “The Lady Is for Burning!”. Fantastic Four v1 #238 (Jan. 1982), Marvel Comics. Byrne, John (w, a). “The Masque of Doom!”. Fantastic Four v1 #268 (Jul. 1984), Marvel Comics. Byrne, John (w, a). “This Land Is Mine!”. Fantastic Four v1 #247 (Oct. 1982), Marvel Comics. (sample). Byrne, John (w, a). “Too Many Dooms”. Fantastic Four v1 #246 (Sep. 1982), Marvel Comics. (sample). Byrne, John (w, a). “When Titans Clash!”. Fantastic Four v1 #260 (Nov. 1983), Marvel Comics. Byrne, John (w, p), Jerry Ordway (i). “Crack of Doom!”. Fantastic Four v1 #279 (Jun. 1985), Marvel Comics. Byrne, John (w, p), Jerry Ordway (i). “True Lies”. Fantastic Four v1 #278 (May. 1985), Marvel Comics. Byrne, John (w, p), Joe Sinnott (i). “Full Circle”. Fantastic Four v1 #288 (Mar. 1986), Marvel Comics. Byrne, John (w, p), Joe Sinnott (i). “Prisoner of the Flesh”. Fantastic Four v1 #287 (Feb. 1986), Marvel Comics. Byrne, John (w, p), Stan Lee (w), Tom Palmer (i). “Escape–To Terror!”. Silver Surfer v2 #1 (Jun. 1982), Marvel Comics. (sample). Carlin, Michael (w), Ron Zalme (p), Al Milgrom (i). “Doctor Doom had a sense of humor”. What If ? v1 #34 (Aug. 1982), Marvel Comics. Claremont, Chris (w), Dave Cockrum (p), Joe Rubinstein (i). “Kidnapped!”. The Uncanny X-Men v1 #145 (May. 1981), Marvel Comics. Claremont, Chris (w), Dave Cockrum (p), Joe Rubinstein (i). “Murderworld!”. The Uncanny X-Men v1 #146 (Jun. 1981), Marvel Comics. Claremont, Chris (w), Dave Cockrum (p), Joe Rubinstein (i). “Rogue Storm!”. The Uncanny X-Men v1 #147 (Jul. 1981), Marvel Comics. (sample). Claremont, Chris (w), John Byrne (p), Terry Austin (i). “Days Of Future Past”. The Uncanny X-Men v1 #141 (Jan. 1981), Marvel Comics. Claremont, Chris (w), John Romita, Jr. (p), Dan Green (i). “To Save Arcade?”. The Uncanny X-Men v1 #197 (Sep. 1985), Marvel Comics. Claremont, Chris (w), Jon Bogdanove (p), Terry Austin (i). “A Matter of Faith”. Fantastic Four vs. X-Men v1 #4 (Jun. 1987), Marvel Comics. Claremont, Chris (w), Jon Bogdanove (p), Terry Austin (i). “Are You Sure?!”. Fantastic Four vs. X-Men v1 #1 (Feb. 1987), Marvel Comics.

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Claremont, Chris (w), Jon Bogdanove (p), Terry Austin (i). “By the Soul’s Darkest Light”. Fantastic Four vs. X-Men v1 #3 (Apr. 1987), Marvel Comics. Claremont, Chris (w), Jon Bogdanove (p), Terry Austin (i). “Truth and Consequences”. Fantastic Four vs. X-Men v1 #2 (Mar. 1987), Marvel Comics. (sample). Conway, Gerry (w), Gene Colan (p), Frank Giacoia (i), Mike Esposito (i). “…And If I Be Called Traitor–!”. Astonishing Tales v1 #7 (Aug. 1971), Marvel Comics. Conway, Gerry (w), Gene Colan (p), Frank Giacoia (i). “The Dreamstone”. Sub-Mariner v1 #49 (May. 1972), Marvel Comics. Conway, Gerry (w), Gene Colan (p), Mike Esposito (i). “Doomsmasque”. Sub-Mariner v1 #47 (Mar. 1972), Marvel Comics. Conway, Gerry (w), Gene Colan (p), Mike Esposito (i). “Twilight of the Hunted”. Sub-Mariner v1 #48 (Apr. 1972), Marvel Comics. (sample). Conway, Gerry (w), Gene Colan (p), Tom Palmer (i). “…Though Some Call It Magic!”. Astonishing Tales v1 #8 (Oct. 1971), Marvel Comics. Conway, Gerry (w), George Perez (p), Pablo Marcos (i). “To Stand Alone!”. The Avengers v1 #155 (Jan. 1977), Marvel Comics. (sample). Conway, Gerry (w), Jim Shooter (w), Sal Buscema (p), Pablo Marcos (i). “The Private War of Doctor Doom!”. The Avengers v1 #156 (Feb. 1977), Marvel Comics. (sample). Conway, Gerry (w), Rich Buckler (p), Frank Giacoia (i). “The Terrible Triumph of Dr. Doom”. Fantastic Four v1 #143 (Feb. 1974), Marvel Comics. Conway, Gerry (w), Rich Buckler (p), Joe Sinnott (i). “Attack!”. Fantastic Four v1 #144 (Mar. 1974), Marvel Comics. Conway, Gerry (w), Rich Buckler (p), Joe Sinnott (i). “No Friend Beside Him”. Fantastic Four v1 #142 (Jan. 1974), Marvel Comics. Cuti, Nick (w), Bill Mantlo (w), Jim Salicrup (w), Winslow Mortimer (p), Mike Esposito (i). “Spider-Man and Web-Man”. Spidey Super Stories v1 #25 (Aug. 1977), Marvel Comics. DeFalco, Tom (w), Alan Kupperberg (p), Bruce Patterson (i). “What If the Avengers Had Fought the Kree-Skrull War Without Rick Jones?”. What If ? v1 #20 (Apr. 1980), Marvel Comics. DeFalco, Tom (w), Frank Springer (p), Danny Bulanadi (i), Armando Gil (pages 10–19). “Here Nightmares Abide!”. Dazzler v1 #4 (Jun. 1981), Marvel Comics. DeFalco, Tom (w), Frank Springer (p), Ricardo Villamonte (i). “Tell Joey I Love Him!”. Dazzler v1 #5 (Jul. 1981), Marvel Comics. DeFalco, Tom (w), John Romita, Jr. (p), Alan Kupperberg (p), Danny Bulanadi (i), Armando Gil (i). “The Jewels of Doom!”. Dazzler v1 #3 (May. 1981), Marvel Comics. (sample). DeFalco, Tom (w), Ron Frenz (p), Bob Layton (i). “With Foes Like These…”. The Amazing Spider-Man v1 #283 (Dec. 1986), Marvel Comics. (sample).

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DeFalco, Tom (w), Ron Frenz (p), Brett Breeding (i). “This Secret Love– !”. Thor v1 #383 (Sep. 1987), Marvel Comics. DeFalco, Tom (w), Ron Wilson (p), Mike Esposito (i). “Visiting Hours!”. Marvel Two-In-One v1 #96 (Feb. 1983), Marvel Comics. (sample). DePatie-Freleng Enterprises (Dec. 1978) “The Final Victory of Doctor Doom”. The New Fantastic Four. DePatie-Freleng Enterprises (Oct. 1978) “The Fantastic Four Meet Doctor Doom”. The New Fantastic Four. Drake, Arnold (w), Marie Severin (a). “Who Says a Carnival Has To Be Good?”. Not Brand Echh v1 #13 (May. 1969), Marvel Comics. Electric Company, The (w), (a). “Deadly is the Doctor Called Doom”. Spidey Super Stories Album v1 #1 (Jun. 1977), Marvel Comics. Englehart, Steve (w), Al Milgrom (p), Romeo Tanghal (i). “Showtime! Lost in Space–Time: Conclusion!”. West Coast Avengers v2 #23 (Aug. 1987), Marvel Comics. (sample). Englehart, Steve (w), Bob Brown (p), Mike Esposito (i), Frank Giacoia (i). “To the Death!”. The Avengers v1 #118 (Dec. 1973), Marvel Comics. Englehart, Steve (w), Dave Cockrum (a). “A Blast from the Past!”. GiantSize Avengers v1 #2 (Nov. 1974), Marvel Comics. (sample). Englehart, Steve (w), George Tuska (p), Billy Graham (i). “Crescendo!”. Hero for Hire v1 #8 (Apr. 1973), Marvel Comics. Englehart, Steve (w), George Tuska (p), Billy Graham (i). “Where Angels Fear To Tread!”. Hero for Hire v1 #9 (May. 1973), Marvel Comics. Englehart, Steve (w), Herb Trimpe (p), Don Perlin (i). “‘…And Be a Villain!’”. Super-Villain Team-Up v1 #5 (Apr. 1976), Marvel Comics. Englehart, Steve (w), Herb Trimpe (p), Jack Abel (i). “Prisoner!”. SuperVillain Team-Up v1 #6 (Jun. 1976), Marvel Comics. Englehart, Steve (w), Herb Trimpe (p), Pablo Marcos (i). “Who Is…the Shroud?”. Super-Villain Team-Up v1 #7 (Aug. 1976), Marvel Comics. Englehart, Steve (w), John Buscema (p), Joe Sinnott (i). “All in the Family!”. Fantastic Four v1 #305 (Aug. 1987), Marvel Comics. Englehart, Steve (w), Keith Giffen (p), Owen McCarron (i). “Escape!”. Super-Villain Team-Up v1 #8 (Oct. 1976), Marvel Comics. Englehart, Steve (w), Marshall Rogers (p), Joe Rubinstein [as Josef Rubinstein] (i). “Free”. Silver Surfer v3 #1 (Jul. 1987), Marvel Comics. Englehart, Steve (w), Paul Neary (p), Tony DeZuniga (i). “Double Double”. Fantastic Four Annual v1 #20 (May 1987), Marvel Comics. Fingeroth, Danny (w), Tom DeFalco (w), Frank Springer (p), Vince Colletta (i). “In the Darkness… a Light!”. Dazzler v1 #10 (Dec. 1981), Marvel Comics. Friedrich, Mike (w), Arvell Jones (a), Keith Pollard (p), Dick Ayers (i). “The Modok Machine!”. Iron Man v1 #74 (May. 1975), Marvel Comics. (sample). Gerber, Steve (w), Alan Weiss (p), Gray Morrow (i). “Kiss”. Marvel Comics Super Special v1 #1 (Dec. 1977), Marvel Comics.

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Gerber, Steve (w), Gene Colan (p), John Tartaglione (i). “Mindstorm!”. Daredevil v1 #100 (Jun. 1973), Marvel Comics. Gerber, Steve (w), Jim Starlin (w), Len Wein (w), Jim Starlin (p), Dan Adkins (i), Don Newton (i), Jim Mooney (i). “Games Godlings Play!”. GiantSize Defenders v1 #3 (Jan. 1975), Marvel Comics. Gillis, Peter (w), Tom Sutton (p), Bruce Patterson (i). “What If Dr. Strange Had Been a Disciple of Dormammu?”. What If ? v1 #18 (Dec. 1979), Marvel Comics. (sample). Gillis, Peter B. (w), Ron Frenz (p), Joe Sinnott (i). “What if Susan Richards Died in Childbirth?”. What If ? v1 #42 (Dec. 1983), Marvel Comics. (sample). Glut, Don (w), Fred Kida (p), Dave Simons (i). “What If Doctor Doom Had Become a Hero?”. What If ? v1 #22 (Aug. 1980), Marvel Comics. Goodwin, Archie (w), Herb Trimpe (p), John Severin (i). “Destination: Nightmare!”. The Incredible Hulk v1 #155 (Sep. 1972), Marvel Comics. Goodwin, Archie (w), John Buscema (p), Joe Sinnott (i). “The Alien, the Ally, and Armageddon”. Fantastic Four v1 #116 (Nov. 1971), Marvel Comics. Grant, Steven (w), Alan Kupperberg (p), Al Gordon (i). ‘What If…the Avengers Were the Last Superheroes on Earth?’. What If ? v1 #29 (Oct. 1981), Marvel Comics. Grant, Steven (w), Don Perlin (p), Bob Layton (i). “What If Iron Man Had Been Trapped In King Arthur’s Time?”. What If ? v1 #33 (Jun. 1982), Marvel Comics. Grant, Steven (w), Win Mortimer (p), Mike Esposito (i). “Doctor Doom Meets Prince Namor!”. Spidey Super Stories v1 #53 (Jul. 1981), Marvel Comics. (sample). Grantray-Lawrence Animation (1966). “Dr. Doom’s Day”. The Marvel Super Heroes . (sample). Grubb, Jeff (w), (a). “Judge’s Book”. Marvel Superheroes Role-Playing Game Judge Book v1 #1 (Mar. 1986), Marvel Comics. Grubb, Jeff (w), (a). “Secret Wars”. Marvel Superheroes Secret Wars RPG v1 #1 (Mar. 1986), Marvel Comics. (sample). Grubb, Jeff (w). “Player’s Book”. Marvel Superheroes Players Book v1 #1 (Mar. 1986), Marvel Comics. Gruenwald, Mark (w), (a). “From The Collector To Dracula”. The Official Handbook Of The Marvel Universe v1 #3 (Mar. 1983), Marvel Comics. Gruenwald, Mark (w), Carmine Infantino (p), Al Gordon (i). “Suddenly… The Shroud!”. Spider-Woman v1 #13 (Apr. 1979), Marvel Comics. (sample). Gruenwald, Mark (w), Ralph Macchio (w), George Perez (p), Gene Day (i). “Happiness Is a Warm Alien!”. Marvel Two-In-One v1 #60 (Feb. 1980), Marvel Comics. Hanna-Barbera (Dec. 1967). “Rama-Tut”. The Fantastic Four. (sample). Hanna-Barbera (Dec. 1967). “The Micro World Of Dr. Doom”. The Fantastic Four.

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Hanna-Barbera (Oct. 1967). “Three Predictions of Dr. Doom”. The Fantastic Four. Hanna-Barbera (Sep. 1967). “The Way It All Began”. The Fantastic Four. Hannigan, Ed (a), Al Milgrom (a). “The Super Fantasy…”. Crazy Magazine v1 #68 (Nov. 1980), Marvel Comics. Hembeck, Fred (w, p), Brent Anderson (i), Terry Austin (i), John Beatty (i), Sal Buscema (i), John Buscema (i), John Byrne (i), John Byrne (i), Dave Cockrum (i), Denys Cowan (i), Gene Day (i), Kerry Gammill (i), Michael Golden (i), Dan Green (i), Bob Hall (i), Klaus Janson (i), Bob Layton (i), Steve Leialoha (i), Bob McLeod (i), Al Milgrom (i), Frank Miller (i), Don Perlin (i), Don Perlin (i), Keith Pollard (i), Marshall Rogers (i), John Romita Jr. (i), John Romita (i), Joe Rubinstein (i), Bill Sienkiewicz (i), Walt Simonson (i), Joe Sinnott (i), Frank Springer (i), Chic Stone (i), Ricardo Villamonte (i), l Vosburg (i), Alan Weiss (i), Ron Wilson (i), Mike Zeck (i), “When Titans Chuckle!”. Fantastic Four Roast v1 #1 (May. 1982), Marvel Comics. (sample). Isabella, Tony (w), George Tuska (p), Bill Everett (p), George Evans (p), Fred Kida (i), Frank Springer (i). ‘Slayers From the Sea!’. Super-Villain TeamUp v1 #1 (Aug. 1975), Marvel Comics. Isabella, Tony (w), Sal Buscema (p), Fred Kida (i). “In the Midst of Life…!”. Super-Villain Team-Up v1 #2 (Oct. 1975), Marvel Comics. (sample). Kirby, Jack (w), Jack Kirby (p), John Romita (i). “Steve Rogers!”. Marvel Treasury Special Featuring Captain America’s Bicentennial Battles v1 #1 (Sep. 1976), Marvel Comics. Kirchner, Paul (w), Paul Kirchner (p), (i). “More Marvel Superheroes That Didn’t Quite Make It”. Crazy Magazine v1 #78 (Sep. 1981), Marvel Comics. Layton, Bob (w, i), David Michelinie (w), John Romita, Jr. (p). “Knightmare”. Iron Man v1 #150 (Sep. 1981), Marvel Comics. Layton, Bob (w, i), David Michelinie (w), John Romita, Jr. (p). “Doomquest”. Iron Man v1 #149 (Aug. 1981), Marvel Comics. Lee, Stan (w) Larry Lieber (w), Dick Ayers (a). “The Threat of the Torrid Twosome”. Strange Tales v1 #106 (Mar. 1963), Marvel Comics. Lee, Stan (w), Don Heck (p), Dick Ayers (i). “Enter…Dr. Doom!”. The Avengers v1 #25 (Feb. 1966), Marvel Comics.# Lee, Stan (w), Don Heck (p), Dick Ayers (i). “From the Ashes of Defeat–!”. The Avengers v1 #24 (Jan. 1966), Marvel Comics. Lee, Stan (w), Fred Kida (p), (i). “Doctor Doom and the Flying Saucer”. The Amazing Spider-Man (newspaper strip) v1 #30 (Mar. 1982), Marvel Comics. (sample). Lee, Stan (w), Gene Colan (p), Dick Ayers (i). “The Fearful Secret of Bucky Barnes!”. Captain America v1 #132 (Dec. 1970), Marvel Comics. Lee, Stan (w), Gene Colan (p), John Tartaglione (i). “Don’t Look Now, But It’s–Dr. Doom!”. Daredevil v1 #37 (Feb. 1968), Marvel Comics.

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Lee, Stan (w), Jack Kirby (p), Chic Stone (i). “A Blind Man Shall Lead Them!”. Fantastic Four v1 #39 (Jun. 1965), Marvel Comics. (sample). Lee, Stan (w), Jack Kirby (p), Chic Stone (i). “The Fantastic Origin of Doctor Doom!”. Fantastic Four Annual v1 #2 (Sep. 1964), Marvel Comics. Lee, Stan (w), Jack Kirby (p), Dick Ayers (i). “A Skrull Walks Among Us!”. Fantastic Four v1 #18 (Sep. 1963), Marvel Comics. Lee, Stan (w), Jack Kirby (p), Dick Ayers (i). “In the Clutches of Doctor Doom!”. Fantastic Four v1 #17 (Aug. 1963), Marvel Comics. Lee, Stan (w), Jack Kirby (p), Dick Ayers (i). “Prisoners of the Pharoah!”. Fantastic Four v1 #19 (Oct. 1963), Marvel Comics. (sample). Lee, Stan (w), Jack Kirby (p), Dick Ayers (i). “The Merciless Puppet Master”. Fantastic Four v1 #14 (May. 1963), Marvel Comics. Lee, Stan (w), Jack Kirby (p), Dick Ayers (i). “The Micro-World of Doctor Doom!”. Fantastic Four v1 #16 (Jul. 1963), Marvel Comics. (sample). Lee, Stan (w), Jack Kirby (p), Dick Ayers (i). “The Return of Doctor Doom!”. Fantastic Four v1 #10 (Jan. 1963), Marvel Comics. Lee, Stan (w), Jack Kirby (p), Dick Ayers (i). “The Undersea Legions of Sub-Mariner!”. Fantastic Four Annual v1 #1 (Sep. 1963), Marvel Comics. Lee, Stan (w), Jack Kirby (p), Dick Ayers (i). ‘The Diabolical Duo Join Forces!’. Fantastic Four v1 #6 (Sep. 1962), Marvel Comics. Lee, Stan (w), Jack Kirby (p), Frank Giacoia (i). “The Battle of the Baxter Building!”. Fantastic Four v1 #40 (Jul. 1965), Marvel Comics. (sample). Lee, Stan (w), Jack Kirby (p), Frank Giacoia (i). “The Greatest Array of Supporting Characters Ever Assembled in One Issue”. Fantastic Four Annual v1 #5 (Nov. 1967), Marvel Comics. (sample). Lee, Stan (w), Jack Kirby (p), Frank Giacoia (i). “The Origin of Sore, Son of Shmodin”. Not Brand Echh v1 #3 (Oct. 67), Marvel Comics. Lee, Stan (w), Jack Kirby (p), Frank Giacoia (i). “The Silver Burper!”. Not Brand Echh v1 #1 (Aug. 1967), Marvel Comics. Lee, Stan (w), Jack Kirby (p), George Roussos (i). “3 Against The Torch!”. Strange Tales v1 #122 (Jul. 1964), Marvel Comics. Lee, Stan (w), Jack Kirby (p), George Roussos (i). “The Master Plan of Doctor Doom!”. Fantastic Four v1 #23 (Feb. 1964), Marvel Comics. Lee, Stan (w), Jack Kirby (p), Joe Sinnott (i). “The Flames of Battle —”.”. Fantastic Four v1 #73 (Apr. 1968), Marvel Comics. (sample). Lee, Stan (w), Jack Kirby (p), Joe Sinnott (i). “Doomsday”. Fantastic Four v1 #59 (Feb. 1967), Marvel Comics. (sample). Lee, Stan (w), Jack Kirby (p), Joe Sinnott (i). “Enter… Doctor Doom!”. Fantastic Four v1 #57 (Dec. 1966), Marvel Comics. Lee, Stan (w), Jack Kirby (p), Joe Sinnott (i). “Meet Doctor Doom!”. Fantastic Four v1 #5 (Jul. 1962), Marvel Comics. Lee, Stan (w), Jack Kirby (p), Joe Sinnott (i). “The Dismal Dregs of Defeat!”. Fantastic Four v1 #58 (Jan. 1967), Marvel Comics. Lee, Stan (w), Jack Kirby (p), Joe Sinnott (i). “The Long Journey Home!”. Fantastic Four v1 #100 (Jul. 1970), Marvel Comics. (sample).

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Lee, Stan (w), Jack Kirby (p), Joe Sinnott (i). “The Name Is Doom!”. Fantastic Four v1 #84 (Mar. 1969), Marvel Comics. Lee, Stan (w), Jack Kirby (p), Joe Sinnott (i). “The Peril and The Power!”. Fantastic Four v1 #60 (Mar. 1967), Marvel Comics. Lee, Stan (w), Jack Kirby (p), Joe Sinnott (i). “The Power and the Pride!”. Fantastic Four v1 #87 (Jun. 1969), Marvel Comics. Lee, Stan (w), Jack Kirby (p), Joe Sinnott (i). “The Victims!”. Fantastic Four v1 #86 (May. 1969), Marvel Comics. Lee, Stan (w), Jack Kirby (p), Joe Sinnott (i). “Within This Tortured Land”. Fantastic Four v1 #85 (Apr. 1969), Marvel Comics. (sample). Lee, Stan (w), Jack Kirby (p), Mike Esposito (i). “The Wedding of Sue and Reed!”. Fantastic Four Annual v1 #3 (Oct. 1965), Marvel Comics. Lee, Stan (w), Jack Kirby (p), Sol Brodsky (i). “The Final Victory of Doctor Doom!”. Fantastic Four Annual v1 #2 (Sep. 1964), Marvel Comics. Lee, Stan (w), Jack Kirby (p), Tom Sutton (i), Marie Severin (i). “The Origin of…The Fantastical Four”. Not Brand Echh v1 #7 (Apr. 1968), Marvel Comics. Lee, Stan (w), Jack Kirby (p), Tom Sutton (i). “The Human Scorch Has To… Meet the Family!”. Not Brand Echh v1 #6 (Feb. 1968), Marvel Comics. Lee, Stan (w), Jack Kirby (p), Tom Sutton (i). “The Origin of Forbush-Man the Way-Out Wonder”. Not Brand Echh v1 #5 (Dec. 1967), Marvel Comics. Lee, Stan (w), Jack Kirby (p), Vince Colletta (i). “Lo! There Shall Be An Ending!”. Fantastic Four v1 #43 (Oct. 1965), Marvel Comics. Lee, Stan (w), Jack Kirby (p), Vince Colletta (i). “When Meet the Immortals!”. Journey Into Mystery v1 #125 (Feb. 1966), Marvel Comics. Lee, Stan (w), John Buscema (p), Joe Sinnott (i). “The Origin of the Silver Surfer!”. The Silver Surfer v1 #1 (Aug. 1968), Marvel Comics. (sample). Lee, Stan (w), John Buscema (p), Joe Sinnott (i). “The Return of the Monster!”. Fantastic Four v1 #124 (Jul. 1972), Marvel Comics. Lee, Stan (w), John Buscema (p), Joe Sinnott (i). “Trapped in Doomsland”. Thor v1 #183 (Dec. 1970), Marvel Comics. Lee, Stan (w), John Buscema (p), John Romita (p), Joe Sinnott (i). “The Prisoner– the Power– and– Dr. Doom!”. Thor v1 #182 (Nov. 1970), Marvel Comics. Lee, Stan (w), John Buscema (p), Sal Buscema (i). “The Heir of Frankenstein!”. The Silver Surfer v1 #7 (Aug. 1969), Marvel Comics. Lee, Stan (w), John Romita (p), Frank Giacoia (i). “Alone – against the Underworld!”. Daredevil v1 #19 (Aug. 1966), Marvel Comics. Lee, Stan (w), John Romita Sr (p), (i). “Return To Reality”. The Amazing Spider-Man (newspaper strip) v1 #10 (Jul. 1977), Marvel Comics. Lee, Stan (w), John Romita Sr (p), (i). “Time Of The Terrorist”. The Amazing Spider-Man (newspaper strip) v1 #1 (Apr. 1977), Marvel Comics. Lee, Stan (w), Steve Ditko (a). “Marked for Destruction by Doctor Doom!”. The Amazing Spider-Man v1 #5 (Oct. 1963), Marvel Comics.

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Lee, Stan (w), Steve Ditko (p), (i). “A Gallery Of Spider-Man’s Most Famous Foes!”. The Amazing Spider-Man Annual v1 #1 (Sep. 1964), Marvel Comics. Lee, Stan (w), Gene Colan (p), Frank Giacoia (i). “The Living Prison!”. Daredevil v1 #38 (Mar. 1968), Marvel Comics. (sample). Lee, Stan (w), Gene Colan (p), Paul Reinman (i). “The Name of the Game Is… Mayhem!”. Daredevil v1 #36 (Jan. 1968), Marvel Comics. Lieber, Larry (w), George Tuska (p), Mike Esposito (i). “A Land Enslaved!”. Astonishing Tales v1 #5 (Apr. 1971), Marvel Comics. Lieber, Larry (w), George Tuska (p), Mike Esposito (i). “The Tentacles of the Tyrant!”. Astonishing Tales v1 #6 (Jun. 1971), Marvel Comics. Lieber, Larry (w), Roy Thomas (w), Frank Giacoia (p), Vince Colletta (i). “This Man…This Demon!”. Marvel Super-Heroes v1 #20 (May. 1969), Marvel Comics. Lieber, Larry (w), Wally Wood (a). “Doom Must Die!”. Astonishing Tales v1 #3 (Dec. 1970), Marvel Comics. Lieber, Larry (w), Wally Wood (a). “The Invaders!”. Astonishing Tales v1 #4 (Feb. 1971), Marvel Comics. (sample). DeMatteis, J.M. (w), Sal Buscema (p), Mike Esposito (i). “The World According to… Faustus!”. Marvel Team-Up v1 #133 (Sep. 1983), Marvel Comics. Mantlo, Bill (w), Art Adams (p), Terry Austin (i). “The Lady and the Unicorn”. Cloak and Dagger v2 #9 (Nov. 1986), Marvel Comics. (sample). Mantlo, Bill (w), Bob Hall (p), Don Perlin (i) Duffy Vohland (i). “A World For the Winning!”. Super-Villain Team-Up v1 #14 (Oct. 1977), Marvel Comics. Mantlo, Bill (w), Bob Hall (p), Don Perlin (i). “Chapter 3: My Ally, My Enemy!”. Super-Villain Team-Up v1 #11 (Apr. 1977), Marvel Comics. (sample). Mantlo, Bill (w), Bob Hall (p), Don Perlin (i). “Death-Duel!”. SuperVillain Team-Up v1 #12 (Jun. 1977), Marvel Comics. (sample). Mantlo, Bill (w), Bob Hall (p), Don Perlin (i). “The Sign of the Skull!”. Super-Villain Team-Up v1 #10 (Feb. 1977), Marvel Comics. Mantlo, Bill (w), Bob Hall (p), Mike Esposito (i). “A World Lost!”. The Champions v1 #16 (Nov. 1977), Marvel Comics. (sample). Mantlo, Bill (w), Bret Blevins (p), Terry Austin (i). “Who’ll Stop The Rain?”. Cloak and Dagger v2 #10 (Jan. 1987), Marvel Comics. Mantlo, Bill (w), Gene Colan (p), Bob Wiacek (i). “Decline and Fall!”. What If ? v1 #21 (Jun. 1980), Marvel Comics. Mantlo, Bill (w), George Tuska (p), Mike Esposito (i) Pablo Marcos (i). “Dreadnight and the Daughter of Creation!”. Iron Man v1 #102 (Sep. 1977), Marvel Comics. Mantlo, Bill (w), Gil Kane (a). “Everyone’s Little in Liddleville!”. Micronauts v1 #41 (May. 1982), Marvel Comics.

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Mantlo, Bill (w), Gil Kane (p), Danny Bulanadi (i). “Home Again, Home Again, Jiggety-Jig!”. Micronauts v1 #43 (Jul. 1982), Marvel Comics. Mantlo, Bill (w), Gil Kane (p), Frank Giacoia (i). “Flashback!”. The Amazing Spider-Man v1 #181 (Jun. 1978), Marvel Comics. Mantlo, Bill (w), Herb Trimpe (p), Jim Mooney (i). “A Time Of Titans!”. Super-Villain Team-Up v1 #4 (Feb. 1976), Marvel Comics. Mantlo, Bill (w), Jim Shooter (p), Sal Trapani (i). “Pawns of Attuma!”. Super-Villain Team-Up v1 #9 (Dec. 1976), Marvel Comics. (sample). Mantlo, Bill (w), John Byrne (p), Mike Esposito (i). “Death Drone!”. The Champions v1 #15 (Sep. 1977), Marvel Comics. Mantlo, Bill (w), Keith Giffen (p), Don Perlin (i). “When Walks the Warlord!”. Super-Villain Team-Up v1 #13 (Aug. 1977), Marvel Comics. Mantlo, Bill (w), Sal Buscema (p), Danny Bulanadi (i). “Turning Point!”. The Incredible Hulk v1 #295 (May. 1984), Marvel Comics. (sample). Mantlo, Bill (w), Sal Buscema (p), Mike Esposito (i) Dave Hunt (i). “Visions of Hate!”. Marvel Team-Up v1 #42 (Feb. 1976), Marvel Comics. Mantlo, Bill (w), Sal Buscema (p), Mike Esposito (i) Dave Hunt (i). “A Past Gone Mad!”. Marvel Team-Up v1 #43 (Mar. 1976), Marvel Comics. Mantlo, Bill (w), Sal Buscema (p), Mike Esposito (i), Dave Hunt (i). “Death in the Year Before Yesterday!”. Marvel Team-Up v1 #44 (Apr. 1976), Marvel Comics. (sample). Mark, Christy (w), (a). “The Fantastic Mr. Frump”. Spider-Man And His Amazing Friends (1981 Cartoon series) v1 #3 (Sep. 1981), Marvel Comics. Martin, Dave E (w). “Fantastic Four Compendium”. Marvel Superheroes Fantastic Four Compendium v1 #1 (Mar. 1987), Marvel Comics. Marvel Productions (Sep. 1982). “Spider-Man: Unmasked!” Spider-Man and His Amazing Friends . (sample). Michelinie, David (w), Bob Hall (a). “Emperor Doom”. Marvel Graphic Novel v1 #27 (Jan. 1987), Marvel Comics. (sample). Milgrom, Al (w), Al Milgrom (a). “Unusu-Al Pin-Ups!”. Marvel Fanfare v1 #11 (Nov. 1983), Marvel Comics. (sample). Mingo, Kolfax (w), Winslow Mortimer (p), Mike Esposito (i). “Star Jaws”. Spidey Super Stories v1 #31 (Feb. 1978), Marvel Comics. Moench, Doug (w), Alan Kupperberg (p), Jim Mooney (i). “A Deal With Darkoth”. Thor v1 #325 (Nov. 1982), Marvel Comics. Moench, Doug (w), Ernie Chan (a). “The Phoenix Gambit Part 2 EndGame”. Master of Kung Fu v1 #60 (Jan. 1978), Marvel Comics. Moench, Doug (w), Mike Zeck (p), (i). “The Phoenix Gambit Part 1 The Temples of Time”. Master Of Kung Fu v1 #59 (Dec. 1977), Marvel Comics. Moench, Doug (w), Tom Sutton (a). “The Power of the People!”. Fantastic Four Annual v1 #15 (Dec. 1980), Marvel Comics. (sample). Mooney, Jim (a). “To Defy Doctor Doom!”. Spider-Woman Bubble Funnies Mini Comic v1 #4 (Jun. 1981), Marvel Comics. Nocenti, Ann (w), Don Perlin (p), Kim DeMulder (i). “Beauty and the Beast Part 1”. Beauty and the Beast v1 #1 (Dec. 1984), Marvel Comics.

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Nocenti, Ann (w), Don Perlin (p), Kim DeMulder (i). “Checkmate”. Beauty and the Beast v1 #4 (Jun. 1985), Marvel Comics. Nocenti, Ann (w), Don Perlin (p), Kim DeMulder (i). “Heartbreak Hotel”. Beauty and the Beast v1 #2 (Feb. 1985), Marvel Comics. Nocenti, Ann (w), Don Perlin (p), Kim DeMulder (i). “Showtime”. Beauty and the Beast v1 #3 (Apr. 1985), Marvel Comics. (sample). O’Neil, Denny (w), Frank Miller (p), Tom Palmer (i). “The Book of the Vishanti”. The Amazing Spider-Man Annual v1 #14 (Dec. 1980), Marvel Comics. (sample). Owsley, Jim (w), Rich Buckler (p), M. Hands (i). “And Then the Gods Cried”. The Spectacular Spider-Man v1 #111 (Feb. 1986), Marvel Comics. Parr, Larry (w), (a). “Canon of Doom”. Spider-Man (1981 Cartoon series) v1 #17 (Jan. 1982), Marvel Comics. Parr, Larry (w), (a). “Countdown to Doom”. Spider-Man (1981 Cartoon series) v1 #21 (Jan. 1982), Marvel Comics. Parr, Larry (w), (a). “The A-B-C’s of D-O-O-M”. Spider-Man (1981 Cartoon series) v1 #12 (Nov. 1981), Marvel Comics. Parr, Larry (w), (a). “The Doctor Prescribes Doom”. Spider-Man (1981 Cartoon series) v1 #3 (Oct. 1981), Marvel Comics. (sample). Parr, Larry (w), (a). “The Doom Report”. Spider-Man (1981 Cartoon series) v1 #19 (Jan. 1982), Marvel Comics. Robert Michelson Inc. (Nov. 1975). “In the Clutches of Doctor Doom”. The Fantastic Four Radio Show. Robert Michelson Inc. (Nov. 1975). “Rama Tut”. The Fantastic Four Radio Show. Robert Michelson Inc. (Oct. 1975). “Return of Doctor Doom”. The Fantastic Four Radio Show. (sample). Robert Michelson Inc. (Sep. 1975). “Dreaded Doctor Doom”. The Fantastic Four Radio Show. Salicrup, Jim (w), Larry Lieber (w), Winslow Mortimer (p), Mike Esposito (i), Tony Mortellaro (i). “Deadly Is the Doctor Called Doom (part 1)”. Spidey Super Stories v1 #19 (Oct. 1976), Marvel Comics. (sample). Salicrup, Jim (w), Michael Siporin (w), E. Webber (w), Winslow Mortimer (p), Ricardo Villamonte (i). “The Sunshine Machine”. Spidey Super Stories v1 #45 (Mar. 1980), Marvel Comics. Scott, Jeffrey (w), (a). “Dr. Doom, Master Of The World”. Spider-Man (1981 Cartoon series) v1 #2 (Sep. 1981), Marvel Comics. Severin, Marie (w), Marie Severin (p), John Tartaglione (i). “Super-Hero Daydreams”. Not Brand Echh v1 #11 (Dec. 1968), Marvel Comics. (sample). Shooter, Jim (w), Al Milgrom (p), Steve Leialoha (i). “Charge of the Dark Brigade!”. Secret Wars II v1 #7 (Jan. 1986), Marvel Comics. (sample). Shooter, Jim (w), Al Milgrom (p), Steve Leialoha (i). “Earthfall!”. Secret Wars II v1 #1 (Jul. 1985), Marvel Comics. Shooter, Jim (w), Al Milgrom (p), Steve Leialoha (i). “Life Rules!”. Secret Wars II v1 #6 (Dec. 1985), Marvel Comics.

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Shooter, Jim (w), Al Milgrom (p), Steve Leialoha (i). “This World Is Mine!”. Secret Wars II v1 #3 (Sep. 1985), Marvel Comics. Shooter, Jim (w), Bob Layton (p), John Beatty (i). “Situation: Hopeless!”. Marvel Super-Heroes Secret Wars v1 #4 (Aug. 1984), Marvel Comics. Shooter, Jim (w), Bob Layton (p), John Beatty (i). “The Battle of Four Armies!”. Marvel Super-Heroes Secret Wars v1 #5 (Sep. 1984), Marvel Comics. Shooter, Jim (w), George Evans (p), Jack Abel (i). “If Vengeance Fails!”. Super-Villain Team-Up v1 #3 (Dec. 1975), Marvel Comics. Shooter, Jim (w), John Buscema (p), Joe Sinnott (i), Terry Austin (i), Klaus Janson (i), Bob McLeod (i), Al Milgrom (i), Steve Leialoha (i), Walt Simonson (i), Bob Layton (i), Joe Rubinstein (i), Bob Wiacek (i). ‘The Murderous Menace of… Doctor Doom and the Parasite!’ Marvel Treasury Edition v1 #28 (Jul. 1981), Marvel Comic. Shooter, Jim (w), Michael Zeck (p), John Beatty (i), Jack Abel (i), Mike Esposito (i). “Invasion”. Marvel Super-Heroes Secret Wars v1 #8 (Dec. 1984), Marvel Comics. Shooter, Jim (w), Michael Zeck (p), John Beatty (i). “Nothing To Fear”. Marvel Super-Heroes Secret Wars v1 #12 (Apr. 1985), Marvel Comics. Shooter, Jim (w), Michael Zeck (p), John Beatty (i). “Prisoners of War!”. Marvel Super-Heroes Secret Wars v1 #2 (Jun. 1984), Marvel Comics. (sample). Shooter, Jim (w), Michael Zeck (p), John Beatty (i). “The War Begins”. Marvel Super-Heroes Secret Wars v1 #1 (May. 1984), Marvel Comics. Shooter, Jim (w), Mike Zeck (p), John Beatty (i). “…And Dust to Dust!”. Marvel Super-Heroes Secret Wars v1 #11 (Mar. 1985), Marvel Comics. Shooter, Jim (w), Mike Zeck (p), John Beatty (i). “A Little Death…”. Marvel Super-Heroes Secret Wars v1 #6 (Oct. 1984), Marvel Comics. (sample). Shooter, Jim (w), Mike Zeck (p), John Beatty (i). “Assault on Galactus!”. Marvel Super-Heroes Secret Wars v1 #9 (Jan. 1985), Marvel Comics. Shooter, Jim (w), Mike Zeck (p), John Beatty (i). “Berserker!”. Marvel Super-Heroes Secret Wars v1 #7 (Nov. 1984), Marvel Comics. (sample). Shooter, Jim (w), Mike Zeck (p), John Beatty (i). “Death to the Beyonder”. Marvel Super-Heroes Secret Wars v1 #10 (Feb. 1985), Marvel Comics. Shooter, Jim (w), Mike Zeck (p), John Beatty (i). “Tempest Without, Crisis Within!”. Marvel Super-Heroes Secret Wars v1 #3 (Jul. 1984), Marvel Comics. Skeates, Steve (w), Joe Albelo (a), Terry Austin (a), John Buscema (a), Dave Cockrum (a), Armando Gil (a), Larry Hama (a), Alan Kupperberg (a), Bob McLeod (a), Marshall Rogers (a). “Do-It-Yourself Comic Book”. Crazy Magazine v1 #82 (Jan. 1982), Marvel Comics. (sample). Steranko, Jim (w), Jim Steranko (p), Joe Sinnott (i). “Armageddon!”. Strange Tales v1 #167 (Apr. 1968), Marvel Comics. (sample). Stern, Roger (w), Al Milgrom (p), Joe Sinnott (i). “The Witch’s Tale!”. The Avengers v1 #234 (Aug. 1983), Marvel Comics. Stern, Roger (w), John Buscema (p), Sal Buscema (i). “Dearly Beloved?”. Fantastic Four v1 #300 (Mar. 1987), Marvel Comics.

APPENDIX B: DOCTOR DOOM CORPUS

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Stern, Roger (w), John Buscema (p), Tom Palmer (i). “The Once and Future Kang!”. The Avengers v1 #269 (Jul. 1986), Marvel Comics. Stern, Roger (w), John Romita Jr. (p), Dan Green (i). “The Daydreamers”. The Amazing Spider-Man v1 #246 (Nov. 1983), Marvel Comics. Stern, Roger (w), Kevin Nowlan (p), Terry Austin (i). “Gather My Disciples Before Me!”. Doctor Strange v1 #57 (Feb. 1983), Marvel Comics. Thomas, Jean (w), Win Mortimer (p), Mike Esposito (i), Tony Mortellaro (i). “The Day of Doom”. Spidey Super Stories v1 #9 (Jun. 1975), Marvel Comics. Thomas, Roy (w), Alan Kupperberg (p), Frank Springer (i). “A Time of Titans!”. The Invaders v1 #33 (Oct. 1978), Marvel Comics. Thomas, Roy (w), Alan Kupperberg (p), Frank Springer (i). “Thunder in the East!”. The Invaders v1 #32 (Sep. 1978), Marvel Comics. Thomas, Roy (w), Dick Ayers (p), John Severin (i). “Sanctuary!”. The Incredible Hulk v1 #143 (Sep. 1971), Marvel Comics. Thomas, Roy (w), Don Heck (p), Werner Roth (p), Vince Colletta (i). “And Time, the Rushing River…”. The Avengers Annual v1 #2 (Sep. 1968), Marvel Comics. Thomas, Roy (w), Gary Friedrich (w), Herb Trimpe (p), John Severin (i). “The Monster and the Madman!”. The Incredible Hulk v1 #144 (Oct. 1971), Marvel Comics. Thomas, Roy (w), Gil Kane (p), Al Milgrom (i). “To Bestride the World”. Giant-Size Super-Villain Team-Up v1 #2 (Jun. 1975), Marvel Comics. Thomas, Roy (w), John Buscema (p), Joe Sinnott (i). “The Way It Began”. Power Records v1 #13 (Oct. 1974), Marvel Comics. (sample). Thomas, Roy (w), John Buscema (p), Joe Sinnott (i). “Divided - We Fall”. Fantastic Four v1 #128 (Nov. 1972), Marvel Comics. (sample). Thomas, Roy (w), John Buscema (p), Joe Sinnott (i). “Encounter At Land’s End!”. Giant-Size Super-Villain Team-Up v1 #1 (Mar. 1975), Marvel Comics. Thomas, Roy (w), John Buscema (p), Joe Sinnott (i). “The Way It Began!”. Fantastic Four v1 #126 (Sep. 1972), Marvel Comics. Thomas, Roy (w), John Buscema (p), Johnny Craig (i). “In the Darkness Dwells…Doom!”. Sub-Mariner v1 #20 (Dec. 1969), Marvel Comics. Thomas, Roy (w), John Buscema (p), Tom Palmer (i). “Come on in… the Revolution’s Fine!”. The Avengers v1 #83 (Dec. 1970), Marvel Comics. Thomas, Roy (w), Keith Pollard (p), Chic Stone (i). “The Twilight of SOME Gods!”. Thor v1 #293 (Mar. 1980), Marvel Comics. (sample). Thomas, Roy (w), Len Wein (w), Rich Buckler (p), Joe Sinnott (i). “Middle Game!”. Fantastic Four v1 #156 (Mar. 1975), Marvel Comics. Thomas, Roy (w), Marie Severin (a). “Comiclot”. Not Brand Echh v1 #12 (Feb. 1969), Marvel Comics. (sample). Thomas, Roy (w), Marie Severin (a). “What Price Forbush-Man?”. Not Brand Echh v1 #8 (Jun. 1968), Marvel Comics. Thomas, Roy (w), Rich Buckler (p), Joe Sinnott (i). “And Now–the End Game Cometh!”. Fantastic Four v1 #157 (Apr. 1975), Marvel Comics.

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Thomas, Roy (w), Tom Sutton (a). “Casey at the Bat”. Not Brand Echh v1 #9 (Aug. 1968), Marvel Comics. (sample). Thomas, Roy (w), Wally Wood (a). “Revolution!”. Astonishing Tales v1 #2 (Oct. 1970), Marvel Comics. Thomas, Roy (w), Wally Wood (a). “Unto You Is Born… The Doomsman!”. Astonishing Tales v1 #1 (Aug. 1970), Marvel Comics. (sample). Wein, Len (w), Rich Buckler (p), Joe Sinnott (i). “Battle Royal!”. Fantastic Four v1 #155 (Feb. 1975), Marvel Comics. Wein, Len (w), Walt Simonson (p), Tony DeZuniga (i). “…Like a Diamond in the Sky!”. Thor v1 #271 (May. 1978), Marvel Comics. (sample). Windsor-Smith, Barry (w), Barry Windsor-Smith (a). “that night…”. Marvel Fanfare v1 #15 (Jul. 1984), Marvel Comics. Wolfman, Marv (w), George Perez (p), Tom Palmer (i). “What If a Criminal Had Become Nova?”. What If ? v1 #15 (Jul. 1979), Marvel Comics. Wolfman, Marv (w), Keith Pollard (p), Joe Sinnott (i). “Invasion!”. Fantastic Four v1 #198 (Sep. 1978), Marvel Comics. (sample). Wolfman, Marv (w), Keith Pollard (p), Joe Sinnott (i). “The Riotous Return of the Red Ghost!”. Fantastic Four v1 #197 (Aug. 1978), Marvel Comics. Wolfman, Marv (w), Keith Pollard (p), Joe Sinnott (i). “The Son of Dr. Doom!”. Fantastic Four v1 #199 (Oct. 1978), Marvel Comics. Wolfman, Marv (w), Keith Pollard (p), Joe Sinnott (i). “When Titans Clash!”. Fantastic Four v1 #200 (Nov. 1978), Marvel Comics. Wolfman, Marv (w), Keith Pollard (p), Pablo Marcos (i). “Who in the World Is the Invincible Man?”. Fantastic Four v1 #196 (Jul. 1978), Marvel Comics. Wolfman, Marv (w), Sal Buscema (p), Tony DeZuniga (i). “The Way It Was”. Fantastic Four v1 #190 (Jan. 1978), Marvel Comics.

Appendix C: Example of Signifier Survey

[Character Name] SURVEY This survey’s main purpose is to discover [Character Name]’s core characteristics—the things that make them [Character Name]. There are questions about this in four sections: to do with [Character Name] themselves, the world they live in [Character Name]’s behaviour in that world, and the people and organisations involved in the creation of their stories. To help you, each question is illustrated with examples for other characters. There is also space for you to add any characteristics of [Character Name] that you do not think have been covered elsewhere. At the start of the survey there are a few questions about your experience of [Character Name]’s media appearances. The whole survey should take about 20 minutes. Please note that the survey is interested in your overall perception of [Character Name] in any media, rather than any specific version. If you can’t think of an answer for any question please leave it blank—finding out if some aspects of [Character Name]’s character are less well known than others is part of the research.

1. Your Experience of [Character Name] The first question is about your own experience of [Character Name] stories. Depending on your answer, there may be some additional questions to follow.

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 M. Hibbett, Data and Doctor Doom, Palgrave Studies in Comics and Graphic Novels, https://doi.org/10.1007/978-3-031-45173-7

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Media Types1 In which of the following media have you experienced [Character Name] stories? Please answer ‘Yes’, ‘No’ or ‘Not sure’ for each question. Comics Movies Animated TV shows Video games Newspaper strips Radio shows Action Figures/Toys Other

Yes/No/Not Yes/No/Not Yes/No/Not Yes/No/Not Yes/No/Not Yes/No/Not Yes/No/Not Yes/No/Not

sure sure sure sure sure sure sure sure

If ‘Yes’ to other: Which other media have you seen featuring [Character Name]? Please enter details in the box below.

Comics2 If ‘Yes’ or ‘Not sure’ to comics: How familiar are you with comics from each of the following decades? Please answer using a scale of one to five, where one is “Not familiar at all” and five is “Very familiar”. 1960–1969 1970–1979 1980–1989 1990–1999 2000–2009 2010–present day

1—Not 1—Not 1—Not 1—Not 1—Not 1—Not

familiar familiar familiar familiar familiar familiar

at at at at at at

all/2/3—Fairly all/2/3—Fairly all/2/3—Fairly all/2/3—Fairly all/2/3—Fairly all/2/3—Fairly

familiar/4/5—Very familiar/4/5—Very familiar/4/5—Very familiar/4/5—Very familiar/4/5—Very familiar/4/5—Very

familiar familiar familiar familiar familiar familiar

Comics Series If ‘Yes’ or ‘Not sure’ to comics: 1 Note The media types listed here were selected for Doctor Doom, the original character used to create this survey. Other media types may be required for other characters. 2 Note The examples in this section all apply to Doctor Doom. Other examples would obviously be required to replace them for other characters.

APPENDIX C: EXAMPLE OF SIGNIFIER SURVEY

251

Which comics series are you aware of [Character Name] appearing in? Please answer ‘Yes’ or ‘No’ for each series. Books Of Doom Dark Reign [Character Name] and the Masters of Evil Doom 2099 Fantastic Four Infamous Iron Man Infinity Gauntlet/Infinity War Not Brand Echh Marvel Superheroes Secret Wars Secret Wars II Secret Wars Spidey Super Stories Super-Villain Team-Up The Amazing Spider-Man The Avengers The X-Men Unbeatable Squirrel Girl What If?

Yes/No Yes/No Yes/No Yes/No Yes/No Yes/No Yes/No Yes/No Yes/No Yes/No Yes/No Yes/No Yes/No Yes/No Yes/No Yes/No Yes/No Yes/No

Which other comics series have you seen featuring [Character Name]? Please enter details in the box below.

Movies If ‘Yes’ or ‘Not sure’ to movies: Which movies do you remember seeing that featured [Character Name]? Please answer ‘Yes’ or ‘No’ for each movie. The Fantastic Four (1994) Fantastic Four (2005) Fantastic Four: Rise Of The Silver Surfer (2007) Fantastic 4 (2015) Other

Yes/No Yes/No Yes/No Yes/No Yes/No

If ‘Yes’ to other: Which other movies have you seen featuring [Character Name]? Please enter details in the box below.

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APPENDIX C: EXAMPLE OF SIGNIFIER SURVEY

Animated TV Shows If ‘Yes’ or ‘Not sure’ to animated TV shows: Which animated TV shows do you remember seeing that featured [Character Name]? Please answer ‘Yes’ or ‘No’ for each animated TV show. The Marvel Super Heroes (1966) Fantastic Four (1967) The New Fantastic Four (1978) Spider-Man (1981) Spider-Man and His Amazing Friends (1981) Fantastic Four: The Animated Series (1994) Spider-Man: The Animated Series (1994 The Incredible Hulk (1996) Fantastic Four: World’s Greatest Heroes (2006) The Super Hero Squad Show (2009) Iron Man: Armored Adventures (2009) The Avengers: Earth’s Mightiest Heroes (2010) Ultimate Spider-Man (2012) Avengers Assemble (2013) Hulk and the Agents of S.M.A.S.H. (2013) Yes/No Other

Yes/No Yes/No Yes/No Yes/No Yes/No Yes/No Yes/No Yes/No Yes/No Yes/No Yes/No Yes/No Yes/No Yes/No Yes/No

If ‘Yes’ to other: Which other animated TV shows have you seen featuring [Character Name]? Please enter details in the box below.

Video Games If ‘Yes’ or ‘Not sure’ to video games: Which video games do you remember seeing or playing that featured [Character Name]? Please answer ‘Yes’ or ‘No’ for each video game. The Amazing Spider-Man and Captain America in Dr. Doom’s Revenge! (1989) Spider-Man: The Video Game (1991) Marvel vs Capcom (1995–2011)

Yes/No Yes/No Yes/No

APPENDIX C: EXAMPLE OF SIGNIFIER SURVEY

Marvel: Ultimate Alliance (2006–2019) Marvel Super Hero Squad (2009–2011) Lego Marvel Super Heroes (2013) Marvel Heroes (2015–2016) Other

253

Yes/No Yes/No Yes/No Yes/No Yes/No

If ‘Yes’ to other: Which other video games have you seen or played featuring [Character Name]? Please enter details in the box below.

[Other Categories For any other media types, please follow the same format as above.]

2. About [Character Name] The next four questions are about [Character Name] themselves. For each question, please enter as many answers as you can think of in the boxes provided. You do not have to enter anything at all if nothing comes to mind, and you can always return to this section later. Appearance In the boxes below please enter as many aspects of [Character Name]’s appearance as you can think of. Examples: for Wonder Woman you might enter “tiara”, “wrist amulets”, “red bustier”, “black hair” or for Superman “blue costume”, “red cape”, “red pants”, “S symbol on chest”.

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Names and Titles In the boxes below please enter ways that [Character Name] is addressed, either by other characters, the narrator or themselves. Examples: for Superman you might enter “Kal El”, “Clark Kent”, “Man Of Steel”, “Man Of Tomorrow” or for Doctor Who “The Doctor”, “Doc”, “Professor”, “John Smith”, “The Oncoming Storm”.

Physical Actions In the boxes below please enter specific physical actions that you associate with [Character Name]. This refers to specific physical actions that are characteristic of them, rather than their general behaviour, which will be asked about in the next question. Examples: for Doctor Who you might enter “runs down corridors”, “uses sonic screwdriver”, “regenerates” or for Spider-Man “climbs walls”, “shoots webs”, “swings through city”.

APPENDIX C: EXAMPLE OF SIGNIFIER SURVEY

255

Dialogue In the boxes below please enter things that [Character Name] regularly says. This refers to the specific phrases that they use—their catchphrases. Examples: for the Thing you might enter “it’s clobberin’ time”, “what a revoltin’ development” or for Sherlock Holmes “elementary my dear Watson”, “the game’s afoot”.

3. [Character Name]’s World The next four questions are about the world that [Character Name] lives in. For each question, please enter as many answers as you can think of in the boxes provided. You do not have to enter anything at all if nothing comes to mind, and you can always return to this section later. Other Characters In the boxes below please enter any other characters who regularly appear in [Character Name]’s stories—their supporting cast. Examples: for Sherlock Holmes you might enter “Doctor Watson”, “Mrs Hudson”, “Professor Moriarty”, “Inspector Lestrade” or for Harry Potter “Hermione Granger”, “Ron Weasley”, “Dumbledore”, “Snape”, “Voldemort”.

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Objects In the boxes below please enter any objects that regularly appear in [Character Name]’s stories. Examples: for Harry Potter you might enter “wand”, “invisibility cloak”, “golden snitch” or for Buffy The Vampire Slayer “stake”, “school bag”.

Locations In the boxes below please enter any places that you associate with [Character Name] stories. Examples: for Buffy The Vampire Slayer you might enter “Sunnydale”, “the Hellmouth”, “school library”, “Spike’s crypt” or for Lara Croft “Croft Manor”, “jungle”, “pyramid”.

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257

Previous Events In the boxes below please enter any important events from the past that are referred to in [Character Name]’s stories. This includes flashbacks or things that [Character Name] or other characters mention in their stories—the personal history that makes them who they are. Examples: for Lara Croft you might enter “plane crash in the Himalayas”, “mother’s death”, “inherits family title”, or for Batman “murder of parents”, “back broken by Bane”, “bat flies through window”.

4. [Character Name]’s Behaviour The next three questions are about the way that [Character Name] behaves in their world. For the first two questions, please enter as many answers as you can think of in the boxes provided. You do not have to enter anything at all if nothing comes to mind, and you can always return to this section later.

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Described Behaviour In the boxes below please enter ways that [Character Name]’s behaviour is described within the stories, either by themselves, other characters or the narrator. These do not have to be the same as the way you personally would describe their behaviour, it is how it is perceived within the world of the stories. Examples: for Doctor Doom you might enter “evil”, “megalomaniac”, “great leader” or for Judge Dredd “merciless”, “cold”, “terse”.

Motivations In the boxes below please enter [Character Name]’s motivations for doing what they do, as described in the stories. Examples: for Spider-Man you might enter “guilt”, “excitement”, “need to take photographs” or for Doctor Doom “desire for power”, “to prove his own superiority”, hatred of superheroes.

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259

Personality Traits This section is slightly different. For each of the ten questions below, rate how much you agree or disagree with each statement on a scale of 1 (disagree strongly) to 5 (agree strongly). This is to record your own view of the character’s personality. Some of the statements may seem silly or irrelevant to [Character Name], but please try to rate them all.

[Character Name] is reserved [Character Name] is generally trusting [Character Name] tends to be lazy [Character Name] is relaxed, handles stress well [Character Name] has few artistic interests [Character Name] is outgoing, sociable [Character Name] tends to find fault with others [Character Name] does a thorough job [Character Name] gets nervous easily [Character Name] has an active imagination

Disagree strongly

Disagree a little

Neither agree nor Agree a little disagree

Agree strongly

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

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5. Creators and Marketing The next two questions are about the real-life people and organisations associated with the creation and marketing of [Character Name]’s stories. For each question, please enter as many answers as you can think of in the boxes provided. You do not have to enter anything at all if nothing comes to mind, and you can always return to this section later. Creators In the boxes below please enter the names of any people or organisations that you associate with the creation of [Character Name]’s stories. Please note that this can refer to anybody who worked on any story, not just the original creators of the character. Examples: for Batman you might enter “Frank Miller”, “Christopher Nolan”, “Bill Finger”, “Adam West” or for Duke Nukem “Todd Replogle”, “George Broussard”, “Allen Blum”.

Marketing In the boxes below please enter the names of any people or organisations that you associate with the marketing of [Character Name]’s stories. This could include names that you have already entered in the previous question but does not have to. Examples: for Duke Nukem you might enter “George Broussard”, “Apogee”, “3D Realms” or for Wonder Woman “George Perez”, “DC Comics”, “Lynda Carter”, “Gail Simone”.

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261

6. Other Associations Is there anything else that you associate with [Character Name] that has not been covered in this survey so far? If so, please enter details in the boxes below. You do not have to enter anything at all if nothing comes to mind, and you can always return to this section later.

Thank You The survey is now complete. Thank you for taking part.

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Wolfman, Marv, Sal Buscema, and Tony DeZuniga. 1978. “The Way It Was.” Fantastic Four (Marvel Comics) 1 (190). Wolk, Douglas. 2021. All of the Marvels. London: Profile Books. Wolk, Douglas. 2021. The Master Plan of Doctor Doom! 22 April. Accessed April 23, 2021. https://voiceoflatveria.com/2021/04/20/9-the-master-plan-of-doctordoom-with-patrick-a-reed/. Wolk, Douglas. 2021. “The Voice Of Latveria.” 19: This Man... This Demon! (with Laura Hudson). 29 June. https://voiceoflatveria.com/2021/06/29/19-this-manthis-demon-with-laura-hudson/. Wolk, Douglas. 2021. Voice of Latveria. Accessed June 2, 2021. https://voiceoflatve ria.com/. Wylie, Phillip. 1930. Gladiator. New York: Alfred A. Knopf. Yang, Chongjun, H-W Yien, Chung-Kang Peng, and Ary Goldberger. 2003. “Information Categorization Approach to Literary Authorship Disputes.” Physica A: Statistical Mechanics and Its Applications 329 (3-4): 473–483. York, Chris. 2018. “The Form of Nostalgia: John Byrne’s Fantastic Four and Frank Miller’s Daredevil.” Bournemouth: Ninth International Graphic Novel and Comics Conference.

Index

0–9 2000AD (comic), 171, 201 20th Century Fox, 96, 101

A Abel, Jack, 238, 246 Aca-fan, 2 Actantial identity, 35 action figures, 14, 73, 81, 82, 250 Actorial identity, 33, 37, 39 Adams, Art, 243 Adams, Scott, 235 Adkins, Dan, 239 agreeableness, 29, 39, 160, 161, 163, 170, 197, 198 Akin, Ian, 235 Ally Sloper, 3, 16 Amateur Press Alliance for Indexing, 60 Amazing Spider-Man Annual (comic), 243, 245 Amazing Spider-Man (newspaper strip), 54, 68, 71, 120, 122, 138, 215, 223, 240, 242 Anderson, Brent, 240 Anthony Kraft, David, 235 Appearance, 1, 3, 12, 20, 24, 25, 34, 35, 41, 51–55, 58–64, 66, 67, 82–85, 89, 91–93, 96, 109, 111–113, 115, 118–121, 128, 130–133, 137, 138, 147, 152, 167, 176, 187, 191, 193, 196, 206,

208–210, 212–215, 217–219, 226, 231–233, 249, 253 Application Programming Interface (API), 60 Archetextuality, 14 Arrowverse, 17 Astonishing Tales (comic), 53, 152, 166, 176, 215, 237, 243, 248 Astro Boy, 16 Attributes, 19–21, 23, 25, 35, 39, 227 Attuma, 37 Auchterlonie, Nigel, 200 Austin, Terry, 235–237, 240, 243, 246, 247 author-functions, 33 authorship, 33, 34, 72, 172, 175, 177, 178, 196, 200, 201, 205, 233 auto-ethnographic approach, 2 Avengers (comic), 37, 53, 66, 67, 83, 145, 176, 237, 238, 240, 246, 247, 251 Axiological identity, 33, 39 Ayers, Dick, 238, 240, 241, 247

B Barta, Hilary, 235 Batman, 1, 19–21, 27, 38, 72, 178, 225, 257, 260 Battleworld, 93, 143, 145–147, 220, 221

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 M. Hibbett, Data and Doctor Doom, Palgrave Studies in Comics and Graphic Novels, https://doi.org/10.1007/978-3-031-45173-7

293

294

INDEX

Baxter Building, 93, 98, 101, 114, 143–147, 218, 220 Bayesian Statistics, 3 Beano, 183–187, 190, 192–194, 200, 203 Beast, 37, 137, 145 Beatty, John, 176, 240, 246 Beauty and the Beast (comic), 244, 245 behaviour, 177 Behavioural components, 41, 211 Bertetti, Paolo, 19, 24, 25, 28, 32, 33, 35–37, 39, 40, 205 Beyonder, 125 BFI-10, 111, 116, 160, 167, 207, 211, 213 Big Five Inventory, 28, 39, 233 blackface, 185 Blevins, Bret, 243 blog, 2, 63, 64, 72, 75, 76, 93, 110, 167, 169, 209 Boaty McBoatFace, 72 Bogdanove, Jon, 236, 237 Bond, James, 18, 27, 34, 38 Boris, 20, 79, 90, 91, 98, 101, 132, 133, 218 Breeding, Brett, 238 Brodsky, Sol, 34, 242 Buckler, Rich, 237, 245, 247, 248 Buffy The Vampire Slayer, 256 Bulanadi, Danny, 237, 244 Buscema, John, 95, 101, 238–240, 242, 246, 247 Buscema, Sal, 237, 240, 242–244, 246, 248 Byrne, John, 2, 91, 95, 98, 101, 140, 175, 176, 219, 235, 236, 240, 244

C cameo, 17, 112, 116, 121, 128, 130–132, 136, 139, 167, 174, 217, 219 canonicity, 18, 19, 33 Captain America, 40, 85, 132–134, 137, 151, 218, 227, 252 Captain America (comic), 240 Captain Marvel, 227 Carlin, Michael, 236 Carol Danvers, 227

cartoons, 3, 4, 9, 12, 14, 16–18, 40, 54, 58, 64, 68, 70, 93, 110, 115, 117, 120–123, 137, 138, 150, 165, 176, 209, 211, 215, 223, 224 Cascading Style Sheets, 74 CBBC, 200 Chan, Ernie, 244 Chaos period of the Marvel Age, 56, 67–69, 115, 122, 126, 133–135, 141, 144–146, 149, 151, 152, 155, 156, 159, 165, 167, 169–173, 176, 177, 196, 212, 213 Chaplin, Charlie, 3, 16 character-building, 33, 34, 205 character development, 130, 131, 157 Character signifiers, 19, 25, 178, 222 Character-specific Components, 35, 41, 73, 131, 152, 172, 177, 207 Charity, 18, 19 Chartered Institute of Public Relations, 73 Claremont, Chris, 11, 36, 95, 101, 236, 237 Cloak and Dagger (comic), 243 close reading, 4, 11, 40, 62, 63, 72, 79, 93, 98, 131, 160, 167, 217 Cockrum, Dave, 236, 238, 240, 246 codification, 21–23, 25, 205, 207 coherence, 3, 5, 13, 19, 41, 121, 130, 131, 161, 167, 178, 198, 222, 224, 226, 234 Colan, Gene, 176, 237, 239, 240, 243 cold war, 52, 170 Colletta, Vince, 238, 242, 243, 247 Comic Book Database, 60, 61 Comics Code, 152 Comics Kingdom, 200 Comics Research Hub, 75 Comics-Scholars-L, 75 Comics studies, 2, 9–11, 60, 223 Comic Vine, 60, 61 computer science, 11 Conan, 27 conscientiousness, 29, 30, 39, 160–162, 170, 198 Consolidation period of the Marvel Age, 56, 67–69, 91, 122, 126, 128, 133–135, 141, 144–147, 149, 152,

INDEX

155, 156, 166, 167, 170, 171, 173, 176, 212, 213, 217 continuity, 15, 19, 58, 59, 115, 149, 150, 176, 178, 196, 201 control room, 114, 115, 143–146, 219, 220 Convergence Culture, 14 Conway, Gerry, 36, 56, 237 Copyright, 20, 35, 121 corpus, 2, 4, 10–12, 25, 28, 36, 40, 51, 55, 57–59, 61–67, 69–71, 79–81, 93, 98, 110, 119, 120, 138, 139, 147, 160, 185, 190, 207–211, 215, 217–219, 231 corpus linguistics analysis (CLA), 25, 66 cover date, 57, 208, 209 Crazy Magazine, 68, 142, 240, 246 Creation period of the Marvel Age, 56, 67–69, 122, 123, 126, 128, 134, 135, 141, 144, 145, 147, 149, 155, 165, 167, 170, 174, 176, 178, 213 credits, 34, 40, 111, 117, 118, 200 Crisis On Infinite Earths, 17 Croft, Lara, 18, 27, 256, 257 Crowdsourcing, 72, 81, 83, 85, 87, 92

D Daredevil, 68, 70, 118, 133, 134, 137, 150 Daredevil (comic), 3, 53, 70, 71, 118, 163, 239, 240, 242, 243 database, 4, 11, 40, 57, 58, 60–64, 66, 69, 74, 76, 109–112, 116, 117, 119, 186, 207, 208, 211, 226, 233 data cleaning, 61, 63, 76, 110, 187, 208, 214 data scraping, 60 dataset, 1, 4, 5, 11, 27, 29, 59–61, 76, 80, 110, 118, 119, 160, 161, 169, 172, 187, 213, 222–225, 228, 231, 234 Day, Gene, 239, 240 Dazzler (comic), 68, 237, 238 DC Extended Universe, 223 DC Thomson, 183, 196, 200, 201 DeFalco, Tom, 175, 237, 238 DeMatteis, J.M., 243 DeMulder, Kim, 244, 245

295

Dennis The Menace, 211, 221, 225, 227, 234 Dennis The Menace (American character), 2, 4, 15, 183, 186, 192, 193, 200 Dennis The Menace (British character), 2, 4, 183, 185, 222 DeZuniga, Tony, 238, 248 Dialogue, 25, 41, 88, 100, 113–115, 119, 128, 130, 131, 189, 191, 210–212, 219, 227, 232 Disney, 22, 40, 96, 101, 225, 226 Distant reading, 63 distribution companies, 173–175 Ditko, Steve, 34, 95, 242, 243 Doctor Doom, 1–5, 9, 11, 15, 17, 18, 20, 21, 23, 25–28, 30, 31, 36, 37, 39, 51–55, 59, 61, 63, 64, 66, 70–74, 76, 79, 81–85, 87, 90, 94, 97, 98, 100, 110, 111, 113, 114, 116, 118, 119, 121–123, 130, 132, 133, 138, 142–144, 147, 148, 151, 152, 154, 160, 164, 167, 172, 176–178, 183, 186, 187, 192, 196–198, 200, 202, 207, 208, 212, 214–217, 219, 220, 222–227, 234, 235, 250, 258 Doctor Doom’s Time Machine, 21, 92 Doctor Strange, 83, 91, 101 Doctor Strange (comic), 247 Doctor Who, 18, 19, 27, 35, 254 Donald Duck, 224–226 Doombot, 77, 88, 91, 92, 98, 99, 101, 140–142, 216, 220 Doomstadt, 93, 101, 143, 145, 146, 161 Drake, Arnold, 238 Duke Nukem, 260

E Eco, Umberto, 20, 150, 196 empirical, 167 empirical data, 56 empirical evidence, 10, 22, 178, 214, 221, 222, 226 Englehart, Steve, 57, 238 epidemiology, 3

296

INDEX

Esposito, Mike, 175, 176, 220, 235, 237–239, 242–247 Ethos, 21–23, 25, 39, 205 Evans, George, 240, 246 Events, 14, 15, 17, 19–21, 23, 25, 30, 38, 79, 80, 94, 98, 115, 116, 131, 148–150, 171, 186, 196, 199, 217, 219, 257 Everett, Bill, 240 Excel, 186, 211 exclusion criteria, 3, 55, 58, 59, 62 Existential identity, 25 Existents, 22, 23, 37, 38 experience, 2, 10, 22, 23, 28, 39, 72–74, 81, 97, 100, 116, 118, 167, 192, 196, 197, 209, 211, 214–216, 221, 222, 249, 250 extraversion, 29, 30, 39, 160, 161, 163, 197, 198 extroversion, 170

F Falcon, 227 fanfiction, 58 Fantastic Four Annual (comic), 37, 59, 93, 217, 238, 241, 242, 244 Fantastic Four (cartoon), 54 Fantastic Four (comic), 2, 3, 26, 27, 51, 54–58, 61, 64, 66, 67, 70, 83, 84, 93, 96, 112, 117–120, 122, 137, 140, 149, 165, 166, 171, 215, 219, 223, 235–242, 246–248, 251 Fantastic Four Radio Show (comic), 54, 64, 119, 208, 245 Fantastic Four Roast (comic), 114, 122, 135, 212, 240 Fantastic Four vs. X-Men (comic), 68 fanzine, 58 Ferdinand, Ron, 200 Fictional identity, 24, 25 fictional modal identity, 206 Field Enterprises, 200 Figurative identity, 25, 35, 206 Fingeroth, Danny, 238 Fitzgerald, F. Scott, 30 Fox studios, 96, 101 Frankenstein, 16

Freeman, Matthew, 14, 33, 34, 40, 41, 205, 207 Frenz, Ron, 237–239 Friedrich, Gary, 247 Friedrich, Mike, 238

G Galactus, 91, 133, 134, 137 Garfield, Andrew, 38, 40 Garvey, Brian, 235 Gauntlets, 28, 85, 86, 99, 119, 141 Gerber, Steve, 238, 239 Giacoia, Frank, 118, 235, 237, 238, 241–244 Giant-Size Avengers (comic), 67, 238 Giant-Size Defenders (comic), 239 Giant-Size Super-Villain Team-Up (comic), 67, 247 Giffen, Keith, 238, 244 Gillis, Peter, 176, 239 Global Transmedia Character Network, 18, 19 Glut, Don, 239 Gnasher, 193 Goblet, 92, 101, 141 Gold disk clasps, 86, 99, 119 Golden, Michael, 240 Goodman, Martin, 174 Goodwin, Archie, 56, 175, 239 Google, 64, 208, 211 Gordon, Al, 239 Gordon, Ian, 183–185, 187, 194, 202 Gotham City, 19 Graham, Billy, 238 Grand Comics Database, 60–63, 67, 69, 115, 118, 207 Grantray-Lawrence Animation, 40, 239 Grant, Steven, 239 Green Cape, 86, 99, 119, 220 Green, Dan, 236, 240, 247 Green Hood, 20, 86, 99, 119 Green tunic/skirt, 86, 99, 119 Grimm, Ben, 76, 78 grounded theory, 12, 13, 32, 38, 80, 118, 207 grouping, 4, 39, 41, 67, 73, 131, 136, 153, 162, 197, 207, 211 Gruenwald, Mark, 235, 239

INDEX

gun, 38, 98, 99, 120

H Hall, Bob, 240, 243, 244 Hama, Larry, 246 Hanna-Barbera, 54, 123, 174, 239, 240 Hannigan, Ed, 240 Harry Potter, 15, 37, 73, 255, 256 Hauptmann, 148–151 Hawley, Noah, 54 Heck, Don, 235, 240, 247 Heli-carrier, 132 Hembeck, Fred, 240 Henderson, Erica, 95 Hero for Hire (comic), 238 Hickman, Jonathan, 95, 98, 101, 219 Hitler, Adolf, 148–151 Holland, Tom, 38, 40 Horton, Ian, 225 Hostess Twinkies, 62 Hulk, 38, 133, 134, 137, 142 Human Torch, 64, 133, 134, 137, 138 hyperdiegesis, 14, 16 Hypertextuality, 14

I Iconography, 19–21, 25, 35 inclusion criteria, 55, 81 incoherence, 19, 131 INDUCKS, 226 Infantino, Carmine, 239 inker, 69, 111, 117, 118, 176, 219, 220 Interdisciplinary, 2, 10 Internet Movie Database (IMDb), 64, 208 Intertextuality, 14 Invisible Girl, 133, 134, 137, 138 Iron Man, 68, 91, 101, 133, 134, 137, 218 Iron Man (comic), 53, 83, 163, 235, 238, 240, 243 Isabella, Tony, 240

J Jameson, J. Jonah, 32 Janson, Klaus, 240, 246

297

Jenkins, Henry, 2, 13–16, 19, 40, 190, 205, 223–225 Jetpack, 88, 100, 125–127 Jones, Louise, 34 Journey Into Mystery (comic), 61, 62, 242 Judge Dredd, 258 Justice League, 17

K Kane, Gil, 243, 244, 247 Kang, 144, 148, 149, 151 Ketcham, Hank, 183, 185, 192, 200 Ketcham, Scott, 200 Kida, Fred, 239, 240 King Features, 200 Kirby, Jack, 40, 52, 77, 95, 96, 98, 101, 125, 140, 175–177, 219, 221, 240–242 Kirbytech, 92, 101, 140–142, 220 Kiss (band), 54, 63 Kissinger, Henry, 171 Kitty (reporter), 138, 139 Klastrup, Lisbeth, 21–25, 36, 38–40, 205 Kristoff, 78, 79, 91, 98, 101, 218 Kupperberg, Alan, 237, 239, 244, 246, 247

L Latveria, 20, 37, 51, 52, 78, 80, 86, 93, 94, 98, 100, 101, 138, 143–149, 151, 161, 167–169, 171, 215, 217, 218, 220, 221 Latverian Lackey, 133, 134, 137, 218 Latverian Peasants, 132–134, 137 Latverian resistance, 132 Layton, Bob, 176, 220, 237, 239, 240, 246 Leather belt, 86, 98, 99, 119, 120, 220 Lee, Stan, 34, 40, 52, 53, 56, 62, 95, 96, 98, 101, 117, 130, 154, 172–178, 219, 221, 225, 236, 240–243 Lego, 85, 253 Leialoha, Steve, 240, 245, 246 Lieber, Larry, 176, 240, 243, 245

298

INDEX

Likert scale, 73 Locations, 17, 20, 23, 36–38, 41, 55, 92, 93, 98, 114, 115, 132, 142, 195, 206, 212, 218–220, 232, 233 Loki, 54, 224 Looney Tunes , 14, 17 Lucas, George, 33

M Macchio, Ralph, 239 machinery, 88, 92, 100, 101, 124, 125, 127, 140–142, 216, 220 MAD, 161–166, 198 Magic, 20, 21, 87, 88, 94, 100, 101, 141 Magneto, 133, 134, 137 Maguire, Tobey, 40 Mantlo, Bill, 175, 176, 237, 243, 244 Marcos, Ferdinand, 171 Marcos, Pablo, 176, 237, 238, 243, 248 Market Authors, 34, 40, 41, 95, 96, 117, 172–177, 200, 233 Marvel Age, 4 Marvel Cinematic Universe, 3, 15, 54, 223 Marvel Comics, 1, 9, 14, 17, 34, 40, 54, 56, 59, 60, 62, 71, 95, 96, 98, 101, 172, 174, 186, 200, 201, 221, 235–248 Marvel Comics Super Special (comic), 54, 63, 67, 238 Marvel Fanfare (comic), 235, 244, 248 Marvel Graphic Novel (comic), 244 Marvel Superheroes Adventure Fold-Up Figures , 235 Marvel Super-Heroes (comic), 243 Marvel Superheroes Fantastic Four Compendium, 244 Marvel Superheroes Players Book, 67, 239 Marvel Superheroes Role-Playing Game Judge Book, 67, 239 Marvel Superheroes Secret Wars, 82, 83, 130, 135, 138, 146, 149, 150, 165, 166, 213, 217 Marvel Super-Heroes Secret Wars (comic), 116, 166, 176, 246 Marvel Superheroes Secret Wars RPG, 67, 239

Marvel Team-Up (comic), 68, 133, 243, 244 Marvel Treasury Edition (comic), 17, 54, 67, 246 Marvel TreasurySpecial Featuring Captain America’s Bicentennial Battles (comic), 240 Marvel Two-In-One (comic), 68, 114, 132, 235, 238, 239 Marvel Unlimited, 62, 211 MASH, 84 Master of Kung Fu (comic), 63, 244 Mattel, 166 McCarron, Owen, 238 McLeod, Bob, 240, 246 Mean Absolute Deviation, 161, 234 MediaDB, 11 Mephisto, 91, 94, 101, 125 Metal armour, 86, 99, 119 Metal Boots/Jet boots, 86, 99 Metal Mask, 86, 99, 119, 220 Metatextuality, 14 MF Doom, 96, 97 Michelinie, David, 240, 244 Micronauts (comic), 243, 244 Microsoft Access, 76, 119, 211 Miles Morales, 17, 227 Milgrom, Al, 236, 238, 240, 244–247 Millennium Cohort Study, 222, 223 Miller, Frank, 20, 240, 245, 260 Mini-Mental State Examination, 223 Minnie The Minx, 193, 196 Mise en scéne, 23, 143 Misty Database, 11 Modal identity, 33, 39 MODOK, 238 Moench, Doug, 244 Montgolfier Group, 75 Mooney, Jim, 239, 244 Moore, Alan, 17, 20 Mortimer, Win, 176, 239, 247 Motivations, 23–27, 30–33, 39, 41, 52, 92, 116, 152, 167–170, 199, 206, 207, 211, 215, 216, 219, 220, 227, 233, 258 Mouth grill, 120 Mr Fantastic, 132–134, 137, 138 Mr Wilson, 193, 194 Mrs Wilson, 188, 193, 195

INDEX

Murray, Bill, 64 MySQL, 110, 119 Mythos, 21–23, 25, 38, 205

N Names and Titles, 25, 35, 41, 78, 109, 113, 119, 121, 211, 217, 227, 232 Namor The Sub-Mariner, 4, 54, 76, 78, 98, 132–134, 137, 145 Nazis, 151 Neary, Paul, 238 neuroticism, 29, 39, 160, 161, 163–167, 170, 177, 198, 201, 213, 221 newspapers.com, 185 Newton, Don, 239 Nocenti, Ann, 244, 245 North American Syndication, 200 North, Ryan, 76, 83, 89, 95, 101, 214 Not Brand Echh (comic), 25, 53, 64, 71, 83, 115, 120, 122, 125, 163–165, 176, 213, 238, 241, 242, 245, 247, 248, 251 Novak, Jim, 175, 176 Nowlan, Kevin, 247

O O’Neil, Denny, 245 O’Neill, Kevin, 201 Objects, 22, 23, 25, 38, 41, 91, 92, 99, 112, 115, 120, 131, 140–142, 152, 184, 186, 194, 195, 202, 211, 224, 232, 233, 256 Openness to experience, 160–162, 170, 197 Ordway, Jerry, 236 origin, 38, 52, 54, 94, 141, 148, 150, 151, 215, 221 Orzechowski, Tom, 175, 176, 219 Other characters, 4, 5, 12, 17, 21, 23–25, 28, 31–33, 36–39, 41, 62, 72, 73, 76, 79, 92, 97, 99, 112–114, 116, 120, 125–127, 130–133, 135, 136, 138, 139, 148, 149, 151, 152, 178, 187, 189, 193, 194, 198, 199, 202, 207, 211, 212, 214, 216, 217, 219–221, 224, 227, 232, 233, 249, 254, 255, 257, 258

299

Owsley, Jim, 245

P Palmer, Tom, 236, 237, 245, 247, 248 paratext, 11, 37, 58, 61, 62, 208 Paratextuality, 14, 15 Parkinson, Nigel, 200 Pearson, Roberta, 19–23, 25, 35–40, 72, 178, 205 penciller, 111, 117, 118, 176 Perceived Behaviour, 39, 41, 116, 166, 167, 207, 211, 212, 215, 222, 233 Perez, George, 237, 239, 248, 260 Perlin, Don, 235, 238–240, 243–245 personality traits, 28, 29, 32, 41, 111, 152, 167, 170, 177, 207, 211, 212, 233 PHP, 74, 110 Physical actions, 35, 41, 90, 92, 100, 109, 115, 119, 123, 125–127, 141, 189, 211, 212, 216, 232, 254 Physical laws, 23, 35 physical punishment, 185 Pinochet, 171 Planet Of The Apes , 15 Pollard, Keith, 238, 240, 247, 248 Potts, Carl, 34 power, 34, 87, 88, 94, 100, 101, 123, 125–127, 131, 142, 143, 147, 162, 167, 168, 170, 215, 216, 220, 221 Power Records, 64, 137, 172–174, 208 Previous events, 17, 38, 41, 79, 87, 115, 131, 146, 148–151, 212, 217, 219, 220, 232 Prince Rudolfo, 138 probability, 65, 147 Production of culture approach, 56 Proper identity, 25 pseudo random numbers, 69 psycholexical approach, 27, 30, 32, 39, 41, 207 p-value, 65

Q qualitative research, 12 quantitative research, 12 Questprobe (video game), 235

300

INDEX

R Rama Tut, 123, 148, 149, 151, 245 randomised order, 70 random numbers, 69, 185 Random stratified sampling, 226 Ray, Frank, 118 Reagan, Ronald, 171, 172 recoding, 61, 67, 80, 95, 100, 116, 130, 153, 154, 158 Recurrent Characters, 19, 20, 23, 25, 37 Relational identity, 25 relational tables, 110 representativeness, 66 Rice Burroughs, Edgar, 34 Richards, Franklin, 91, 101 Richards, Reed, 39, 51, 52, 76, 78, 80, 88–91, 94, 98, 101, 132, 148, 151, 168, 215–217, 222 Rivets, 52, 85, 86, 99, 113, 119, 120 Robert Michelson Inc, 245 Robin Hood, 51 Romita Jr, John, 240, 247 Romita Sr, John, 34, 235, 242 Rosen, Joe, 175, 176, 219, 221 Rosen, Sam, 175, 176 Roth, Werner, 247 Roussos, George, 241 royal, 141, 142 Rubinstein, Joe, 236, 238, 240, 246 Ryan, Marie-Laurie, 22, 23, 25, 35–40, 205

S Salicrup, Jim, 175, 237, 245 sampling, 4, 54, 66, 86, 155, 209, 210, 224, 226 Scarred face, 80, 86, 99, 120 Scheele, Christie, 176 Scolari, Carlos, 40, 205 Secret Wars II (comic), 68, 71, 83, 125, 212, 245, 246, 251 semiotics, 205 Setting, 19–23, 25, 36, 38, 55, 60, 110, 131, 142–147, 152, 194, 195, 202, 210, 211, 219, 224, 232 Severin, John, 239, 247 Severin, Marie, 62, 176, 238, 242, 245, 247

Shelley, Mary, 16 Sherlock Holmes, 20, 27, 73, 226, 227, 255 SHIELD, 132 Shooter, Jim, 34, 56, 95, 101, 149, 152, 166, 171, 175, 176, 213, 219, 221, 237, 244–246 Shroud, 138 signifier, 4, 5, 18–20, 22, 23, 25, 28, 34–38, 41, 71, 72, 85, 92, 97, 99, 100, 102, 109–115, 118, 120, 123, 125, 126, 128, 129, 131, 132, 141–143, 146, 147, 152, 159, 168, 174, 175, 177, 178, 187, 205, 212–214, 217, 219, 220 Silver Surfer, 91, 101, 133, 134, 137, 138, 141 Silver Surfer (comic), 53, 139, 143, 236, 238, 242 Simek, Artie, 175, 176, 219, 221 Simonson, Walt, 95, 98, 101, 219, 240, 246, 248 Sinnott, Joe, 95, 101, 175, 176, 220, 221, 235–242, 246–248 Skeates, Steve, 246 slipper, 190 Smallville, 17 Social rules and values, 23, 39 space, 14, 22, 24, 36, 93, 101, 142–146, 193, 220, 232 Spatial/temporal Identity, 36 Spectacular Spider-Man (comic), 245 speech patterns, 113 Spider-man, 3, 17, 20, 32, 34–38, 40, 54, 68, 84, 91, 101, 133, 134, 137, 139, 147, 150, 152, 176, 220, 227, 252, 254, 258 Spider-Man: Into The Spider-Verse, 17 Spider-Man And His Amazing Friends (cartoon series), 84, 120, 121, 223, 244, 252 Spider-Man and the Incredible Hulk (comic), 235 Spider-Man (cartoon series), 3, 9, 17, 54, 93, 120–122, 137, 165, 215 Spider-Woman Bubble Funnies Mini Comic, 244 Spider-Woman (comic), 239 Spidey Super Stories, 28, 116, 164–166

INDEX

Spidey Super Stories Album, 54, 208 Spidey Super Stories (comic), 238 Springer, Frank, 237, 238, 240, 247 SQL, 60, 74 Square eye holes, 86, 99, 119 Squirrel Girl, 89, 91, 101 Starlin, Jim, 239 Star Wars , 15, 33 statistical analysis, 13, 25, 65 statistical significance, 65, 209, 210 Steranko, Jim, 246 Stern, Roger, 40, 95, 101, 246, 247 Stilt Man, 131, 132 Stone, Chic, 240, 241, 247 Storm, Johnny, 76, 78 Story/worlds/media, 22, 205 storyworld, 1–4, 11, 14–17, 19–23, 32–34, 36–39, 41, 53–56, 58, 62, 72, 90, 91, 131, 132, 135–138, 140, 142–144, 147, 149–152, 166, 172, 177, 186, 188, 191–194, 196–198, 202, 205–207, 212, 213, 218, 222–225, 231–233 Storyworld-specific components, 37, 41, 73, 152, 194 Strange Tales (comic), 53, 240, 241, 246 strata, 70 Stratified Random Sampling, 70, 231 Sub-Mariner (comic), 53, 237, 247 Sue Richards, 90, 91 Sue Storm, 90 Superman, 17, 20, 54, 253, 254 super-villain, 1, 2, 54, 62, 125, 154, 156, 158, 167, 225 Super-Villain Team-Up (comic), 54, 67, 83, 133, 152, 165, 166, 176, 200, 238, 240, 243, 244, 246, 251 Supreme, 17 Sure Start, 65, 223 survey, 4, 5, 26, 29, 30, 65, 69, 72–76, 79–81, 83–85, 87, 97–100, 111, 113, 115, 118, 122, 128, 132, 141, 143, 168, 176, 177, 187, 210, 212–220, 222, 225, 231, 234, 249, 250, 261 Survey Monkey, 74 Sutton, Tom, 239, 242, 244, 248

301

T Tanghal, Romeo, 238 Tartaglione, John, 239, 240, 245 Tarzan, 16, 34 technological sublime, 125 text-mining, 27 Textual authors, 34, 40, 41, 54, 111, 116–118, 159, 173–177, 200, 207, 212, 213, 219, 220, 222, 227, 233 The Amazing Spider-Man (comic), 53, 58, 83, 85, 138, 237, 242, 244, 247, 251, 252 The Books Of Doom (comic), 59, 148 The Champions (comic), 243, 244 The Claremont Run, 11, 12, 212 The Defenders (comic), 62, 235 The Doctor Doom Cast , 60 The Flash, 17, 224 The Graphic Narrative Corpus , 11 The Incredible Hulk (comic), 53, 239, 244, 247 The Invaders (comic), 243, 247 TheLatverian Embassy (website), 17, 59, 98, 219 The Lord Of The Rings , 15 The Marvel Age, 52, 56, 57, 60, 67, 83, 85, 93, 132, 140, 147, 170, 172, 176–178, 186, 208, 213, 216–219 The Marvel Chronology Project (website), 59–61 The Marvel Database, 60, 61, 91 The Marvel Super Heroes (comic), 57, 239 The Marvel Superheroes (series), 53, 54, 58, 59, 64, 89 Thematic identity, 35 The New FantasticFour (cartoon), 54, 84, 238, 252 The Official Handbook Of The Marvel Universe (comic), 239 The Red Skul, 54 The Thing, 36, 68, 86, 100, 132, 137, 148, 151, 217, 220, 255 The Thing (comic), 132, 235 Thomas, Jean, 247 Thomas, Roy, 36, 56, 95, 101, 175, 176, 243, 247, 248 Thon, Jan-Noël, 18, 19, 40, 205 Thor, 68, 133, 134, 137

302

INDEX

Thor (comic), 3, 132, 219, 238, 242, 244, 247, 248 throne, 80, 92, 101, 125–127, 140–146, 150 Topos, 21–23, 25, 36, 205 Tosca, Susana, 21–25, 36, 38–40, 205 Transmedia, 1–4, 9, 10, 12–22, 24–27, 33, 34, 37, 38, 40, 41, 55, 58, 64, 71–73, 109, 118, 121, 122, 130, 131, 138, 140, 141, 152, 157, 159, 172, 177, 178, 183, 184, 186, 196, 199, 201, 202, 205, 207–210, 212, 213, 215, 221, 223–227, 231 Transmedia Archaeology, 3, 15, 16, 40 Transmedia storytelling, 2, 3, 13–16, 34 transtextuality, 14 Trimpe, Herb, 238, 239, 244, 247 Tuska, George, 238, 240, 243 twitter, 75, 214 U UAL Archives and Special Collections, 62 Ultimate Spider-Man, 17, 84, 252 Unbeatable Squirrel Girl (series), 83, 85, 95, 97, 214, 251 United Nations, 37, 93, 99, 146, 147, 210, 220 Uricchio, William, 19–23, 25, 35–40, 72, 178, 205 V Valeria, 79, 90, 91, 101, 132, 133 video games, 3, 15, 16, 54, 73, 84, 85, 250, 252, 253 Vietnam, 170 Viewing screen, 4, 23, 93, 99, 115, 140–142, 216, 220 Villamonte, Ricardo, 237, 240, 245

Visual Language Research Corpus , 11 Voice Of Latveria, 60 W Waid, Mark, 95, 98, 101, 216, 219 wandering characters, 1, 148, 224 Warcraft , 15 Watergate, 170 Wein, Glynis, 34, 175, 176, 219 Wein, Len, 56, 239, 247, 248 Weiringo, Mike, 95, 98, 101, 219 Weiss, Alan, 238, 240 West Coast Avengers (comic), 66, 114, 238 What If? (comic), 83, 122, 164, 166, 176, 236, 237, 239, 243, 248 What Were Comics?, 11, 212 Whizzer, 37, 145 Wiacek, Bob, 243, 246 Wikipedia, 208 Wilson, Ron, 184, 195, 235, 238, 240 window, 21, 38, 184, 209 Windsor-Smith, Barry, 248 Wolfman, Marv, 56, 248 Wolverine, 138 Wonder Woman, 253 Wood, Wally, 118, 176, 243, 248 world-building, 33, 34, 205 World War II, 149–151, 171 writer, 11, 33, 34, 57, 60, 95, 111, 117, 130, 166, 171, 173, 176, 201, 212, 213, 219, 220, 227 Y Yellow Kid, 3, 16 Z Zeck, Mike, 40, 176, 240, 244, 246