He‐said–she‐said: Contrast effects in credibility assessments and possible threats to fundamental principles of criminal law
Bibliographic record
Abstract
Purpose. Some criminal trials turn on evaluations of credibility of the complainant and the accused. When credibility is based on how a witness testifies, an evaluation of one party should not influence an evaluation of the other party. If credibility evaluations are bidirectional, fundamental principles of criminal law may be offended. Methods. Six hundred and thirty seven undergraduates read a vignette that described a sexual assault (SA) or a motor vehicle accident (MVA). Karen, the complainant in the SA case and bystander witness in the MVA case, was described as 5, 13, or 20 years old. The vignette was a summary of the police investigation and, in three conditions, the trial. Trial information was manipulated in one of three ways: no information concerning how Karen testified, Karen's testimony was described positively (pro‐prosecution), or described negatively (anti‐prosecution). Participants then rated the perceived credibility of Karen and the accused (Bob) and the probability that Bob was guilty. Results. Karen was viewed more positively in the pro‐prosecution condition and more negatively in the anti‐prosecution. When Karen was judged to be less credible, Bob was rated as more credible and less likely to be guilty. When Karen was seen as more credible, Bob was viewed as more likely to be guilty. Conclusions. This bidirectional effect that the manner in which the prosecution witness testified affected perceptions of the accused and probability of guilt, in certain circumstances, may compromise fundamental principles of criminal law and be a reversible error. We offer possible solutions for future empirical testing.
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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 | 0.006 | 0.085 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 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".