Bibliographic record
Abstract
OBJECTIVE: To examine the prevalence of verbalizations about mental illness in the animated feature films of The Walt Disney Company (TWDC). We discuss the results within the context of children's repeated exposure to popular animated movies and their learning of labels and stereotypes associated with mental illness. We recommend further research on this topic. METHOD: We coded 34 animated feature films produced by TWDC for mental illness references (for example, "crazy" or "nuts"). We developed a coding manual to systematize the content analysis, to ensure accuracy of the data, and to ascertain intercoder reliability. RESULTS: Most of the films (that is, 85%) contain verbal references to mental illness, with an average of 4.6 references per film. The references were mainly used to set apart and denigrate the characters to whom they referred. Twenty-one percent of the principal characters were referred to as mentally ill. We discuss the contributions and limitations of the study. CONCLUSIONS: The findings have implications for child viewers in terms of their potentially learning prejudicial attitudes and distancing behaviours toward individuals perceived as being mentally ill. To further verify this connection, an assessment of the incidence of Disney film exposure and attitudes toward people with a mental illness, using a sample of school-aged children, is needed.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".