Admissibility of Prior Sexual History Evidence: Examining Its Impact on Mock‐Jurors’ Judgments When Gender and Race Are Considered
Bibliographic record
Abstract
Rape shield laws restrict the admission of prior sexual history evidence (PSHE) in sexual assault trials in various countries, including Canada and the U.S. Despite such laws, admission of PSHE is often at the discretion of a trial judge. The current study examined the effect of PSHE (present, absent), victim and defendant gender (male, female), and victim race (White, Indigenous) on mock-juror decision-making. Undergraduate students (N = 484) read a mock-trial transcript depicting a rape case. Mock-jurors provided guilt ratings and perceptions of the victim and defendant. Mock-jurors assigned higher guilt ratings, held less favourable perceptions of the defendant, and more favourable perceptions of the victim, when PSHE was absent. Mock-jurors also were more likely to reach a guilty verdict when the victim was male (as opposed to female). Finally, mock-jurors perceived the defendant less favourably, and the victim more favourably, when the victim was Indigenous, as opposed to White.
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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 | 0.023 | 0.167 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".