Abuse Is Abuse: The Influence of Type of Abuse, Victim Age, and Defendant Age on Juror Decision Making
Bibliographic record
Abstract
= 556) read a trial transcript in which a soccer coach was accused of sexual abuse or physical abuse against a player. The victim's age (child, adolescent, or young adult), the defendant's age (young, middle age, or older adult), and the type of abuse (sexual or physical) were varied. Mock jurors provided a dichotomous and continuous verdict and rated their perceptions of the victim and the defendant. Although no differences on mock jurors' dichotomous verdict were found due to victim age, defendant age, or type of abuse, mock jurors provided higher guilt ratings when the abuse was sexual and both the victim and defendant were described as young adults. Similarly, mock jurors rated the victim more positively when the victim was described as a young adult (vs. child) for both sexual and physical abuse cases, and rated the defendant more positively when the victim was described as a child compared with young adult in sexual abuse cases. These findings suggest that mock jurors were largely influenced by victim age, particularly when the victim was described as an adult compared with a child.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".