Jurors’ perceptions of scientific testimony: The role of gender and testimony complexity in trials involving DNA evidence
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
With continuous advancements in forensic science, expert testimony has become more common in criminal proceedings. This study (N = 170) sought to examine the combined influence of mock juror gender, expert gender, and testimony complexity in a case involving DNA (deoxyribonucleic acid) evidence. Findings revealed that testimony complexity interacted with expert gender to influence verdict judgments. Participants were unaffected by testimony complexity when the expert was a man, but were more likely to convict when complex testimony was presented by a woman. In support of the heuristic-systematic model, expert gender elicited an effect only in high-complexity conditions—interestingly, this was exclusively the case for male mock jurors. Understanding how jurors cognitively process legal and extra-legal information may help legal actors (e.g., evidence experts, lawyers) communicate evidence and its legal relevance more effectively.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it