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Record W4321372768 · doi:10.3390/soc13020048

How He Got His Scars: Exploring Madness and Mental Health in Filmic Representations of the Joker

2023· article· en· W4321372768 on OpenAlex
Jeff Preston, Lindsay Rath-Paillé

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSocieties · 2023
Typearticle
Languageen
FieldHealth Professions
TopicFilm in Education and Therapy
Canadian institutionsThe King's UniversityWestern University
Fundersnot available
KeywordsComicsNarrativeMental illnessMental healthArtAestheticsHistorySociologyLiteraturePsychologyPsychoanalysisPsychiatry

Abstract

fetched live from OpenAlex

In May of 1939, DC Comics introduced their popular Batman series, but it was a year later when the iconic villain, the Joker, entered the story. What began as a lighthearted pulp comic has since evolved, with Batman’s enemies growing darker and more sinister. In the film, the Joker is now less “clown prince” than violent madman, determined to wreak havoc and spread his warped view of society. Through a thematic discourse analysis, this article explores how Batman films featuring the Joker routinely naturalize and reinforce sanist beliefs about mental illness and are deployed as narrative prostheses to rationalize his heinous crimes. Blending work from both disability studies and mad studies, we explore the cultural construction of madness as animated by filmic representations of the Joker and consider how these narratives inform perceptions of mental illness and subsequently rationalize the disciplining of mad people.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.412
Threshold uncertainty score0.449

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.161
GPT teacher head0.448
Teacher spread0.288 · 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