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Record W2065448849 · doi:10.1080/21504857.2014.889731

‘Batman is all about contingency planning’: an interview with American comic-book writer Chuck Dixon

2014· article· en· W2065448849 on OpenAlexaff
Jeffery Klaehn

Bibliographic record

VenueJournal of Graphic Novels & Comics · 2014
Typearticle
Languageen
FieldArts and Humanities
TopicComics and Graphic Narratives
Canadian institutionsEmmanuel Bible College
Fundersnot available
KeywordsComicsKnightArtSupermanVisual artsArt historyLiteratureMedia studiesSociology

Abstract

fetched live from OpenAlex

Chuck Dixon has been working for more than 20 years as a full-time comic-book writer. His first regular assignments were Airboy for Eclipse and the lead feature on Savage Sword of Conan for Marvel Comics. Since that time he has written well over a thousand comic scripts for a range of publishers, including Eclipse, Marvel, DC, Dark Horse, CrossGen, Dynamite Entertainment and IDW. His writing credits include acclaimed runs on Batman, Detective Comics, Catwoman, Robin, Birds of Prey, Nightwing, Green Arrow, The ‘Nam, The Punisher, The Punisher Journal, The Punisher War Zone, Sigil, Crux, Simpsons Comics, G.I. Joe, and a range of other titles. In addition, he is the co-creator (with artist Graham Nolan) of Bane, the Batman villain featured prominently in Christopher Nolan’s film The Dark Knight Rises (2012). This interview explores a range of questions and topics, including important influences on his career as a professional comic-book writer; the origins of the comic industry; elements that make for great superhero comics; continuity as an instructive framework for both comic-book readers and writers; how the idea of ‘creator rights’ has changed over the past 10–15 years; the business of publishing and the emergent dominance of the graphic novel and trade paperback formats; the direct market and the significance of Diamond’s distribution monopoly; Batman’s relationship to Superman and the rest of the DC Universe; and, of course, what elements make for a ‘classic’ Batman story.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.848
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.041
GPT teacher head0.269
Teacher spread0.228 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2014
Admission routes1
Has abstractyes

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