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Record W2792756038 · doi:10.3138/utq.87.1.110

The Difference between Heroes and Monsters: Marvel Monsters and Their Transition into the Superhero Genre

2018· article· en· W2792756038 on OpenAlexvenueno aff
Christopher McGunnigle

Bibliographic record

VenueUniversity of Toronto Quarterly · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicComics and Graphic Narratives
Canadian institutionsnot available
Fundersnot available
KeywordsMonsterComicsRhetoricArchetypeLiteratureArtNarrativeAestheticsAmbiguityPhilosophyLinguistics

Abstract

fetched live from OpenAlex

The monster is an amorphous and ambiguous manifestation of social values, representing fear and revulsion of a cultural Other while engendering escapist power fantasies. The monster era of Marvel Comics from the late 1950s to the early 1960s created a new mediation of the monster based heavily on the comic book industry's desire for a model of mass producible creative content. Graphic narrative's hybridization of verbal and visual rhetoric captures the cultural fluidity of the monster by using multimedia signifiers of ambiguity and reiterating popular visual archetypes. However, Marvel monster categories are as much a result of economic and visual limitations of the comic book medium. Marvel monster rhetoric, in turn, transformed the dichotomy of fear and desire at the heart of the monster into a new superheroic archetype. This article explores the verbal and visual rhetoric of the monster era and what rhetorics transferred to Marvel's silver age superheroes of the 1960s.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0140.031
Scholarly communication0.0140.007
Open science0.0010.007
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0040.000

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 head0.011
GPT teacher head0.177
Teacher spread0.166 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations6
Published2018
Admission routes1
Has abstractyes

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