The Effects of Victim Gender, Defendant Gender, and Defendant Age on Juror Decision Making
Bibliographic record
Abstract
Mock jurors provided credibility ratings for a victim (12 years old) and defendant when victim gender, defendant gender, and defendant age (15 vs. 40 years old) were manipulated. Verdicts and sentence recommendations also were assessed. Higher guilt ratings were found for a male versus female defendant. Juror gender was examined as a covariate in the analyses. Female jurors rated the victim higher on accuracy, truthfulness, and believability than male jurors. Male jurors rated the defendant higher on reliability, credibility, truthfulness, and believability than female jurors. Male jurors perceived the victim to desire and cause the crime to a greater extent than female jurors. Mock jurors rated the victim as more responsible for the crime with an older versus younger defendant. Female jurors ascribed higher responsibility to the defendant compared to male jurors. The younger versus older defendant was perceived to have desired the event but only when the victim was female versus male.
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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 | 0.008 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.006 | 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".