How He Got His Scars: Exploring Madness and Mental Health in Filmic Representations of the Joker
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.018 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".