Are adverse childhood experiences scores associated with heroism or villainy? A quantitative observational study of Marvel and DC Cinematic Universe characters
Bibliographic record
Abstract
Many superhero and villain stories include trauma, which could influence how the public perceives the impact of trauma in their own lives. Our aim was to assess whether total Adverse Childhood Experiences (ACEs) scores were associated with heroism or villainy among Marvel and DC Characters. We watched 33 films, with a total runtime of 77 hours and 5 minutes. We scored 28 characters (19 men, 8 women, and 1 gender fluid). ACEs scores were evenly distributed across heroes/villains (U = 88, z = -.465, p = .642), Marvel/DC universes (U = 95.5, z = -0116, p = .907), and gender (U = 61, z = -.979, p = .328). There was no statistically significant correlation between ACEs scores and status (r(26) = .090, p = .65), universe (r(26) = .022, p = .91), or gender (r(26) = -.188, p = .34). We found that there was no association between ACEs scores and heroism or villainy. Thus, no one is doomed to be a villain just because of early childhood experiences.
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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 | 0.005 |
| 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) | 0.002 | 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".