Evaluating Mock Jurors’ Judgements on Eyewitness Age, Inconsistencies and Crime Type
Bibliographic record
Abstract
Victim testimony is a persuasive form of evidence presented in criminal trials that plays an essential role in influencing verdict outcomes despite eyewitnesses' making inconsistent statements (Innocence, 2022). The current study examined the influence of victim age (12, 42, 72), number of inconsistencies in testimony (4, 9), and crime type (hit, threatened with a weapon) in a home invasion case. Mock jurors read a mock trial transcript and were asked to deliver a verdict, provide a continuous guilt rating, provide a sentence recommendation (if found guilty), and rate their perceptions of the victim. While neither the independent variables in isolation, nor the two-way interactions, influenced verdict or sentencing decisions, a three-way interaction emerged for victim perceptions. Overall, the results suggest that there are certain contexts in which older victims may be perceived as more credible than younger victims in the criminal justice system. Implications and future directions are discussed.
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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 | 0.012 | 0.091 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.004 | 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".