The Effect of Delayed Reporting on Mock-Juror Decision-Making in the Era of #MeToo
Bibliographic record
Abstract
The #MeToo movement has given voice to victims of sexual harassment and assault. In many of these cases, there have been long delays in reporting of the sexual offence (e.g., the Harvey Weinstein case). The purpose of this study was to examine how the type of sexual offence (harassment vs. assault) and the length of delayed reporting (15, 25, 35 years) influenced mock-juror decision-making. Mock-jurors ( N = 319) read a mock trial transcript depicting an alleged sexual offence and were asked to render a dichotomous verdict, continuous guilt rating, and defendant and victim perception ratings. The data indicated an effect of sexual offence type such that mock-jurors held more favorable perceptions of the defendant when the alleged offence was harassment compared with assault. There also was an effect of delayed reporting such that mock-jurors rendered more guilty verdicts when there was a 25-year delay compared with a 15-year delay. Intriguingly, these results suggest that jurors in sexual offence cases may perceive longer delays in reporting as more believable than shorter delays.
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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 | 0.013 | 0.168 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".