Mock-Jurors’ Judgements in a Sexual Assault Case: The Influence of Defendant Race and Occupational Status, Delayed Reporting, and Multiple Allegations
Bibliographic record
Abstract
Reports of sexual offences have increased in recent years, with many cases involving allegations against high-status individuals (e.g., Harvey Weinstein, Bill Cosby). In addition, many of these cases have involved allegations against the defendant from multiple victims, with long delays in reporting of the alleged assault. The purpose of this study was to examine the influence of defendant occupational status (low vs. high), defendant race (White, Black), number of allegations (one vs. five victims), and the length of reporting delay (5, 20, or 35 years) on mock-juror decision-making. Mock-jurors ( N = 752) read a mock-trial transcript describing a sexual assault case. After reading the trial transcript, mock-jurors were asked to provide dichotomous and continuous guilt ratings, as well as ratings regarding their perceptions of the defendant and victim. Results revealed that mock-jurors rendered more guilty verdicts, assigned higher guilt ratings, and perceived the defendant less favorably and the victim more favorably, when the defendant was White (as opposed to Black) and when there were multiple allegations against the defendant. The current findings suggest that defendant race and the number of allegations are highly influential in the context of a sexual assault case.
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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 | 0.011 | 0.119 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".