Educating Jurors about Forensic Evidence: Using an Expert Witness and Judicial Instructions to Mitigate the Impact of Invalid Forensic Science Testimony
Why this work is in the frame
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Bibliographic record
Abstract
Invalid expert witness testimony that overstated the precision and accuracy of forensic science procedures has been highlighted as a common factor in many wrongful conviction cases. This study assessed the ability of an opposing expert witness and judicial instructions to mitigate the impact of invalid forensic science testimony. Participants (N = 155) acted as mock jurors in a sexual assault trial that contained both invalid forensic testimony regarding hair comparison evidence, and countering testimony from either a defense expert witness or judicial instructions. Results showed that the defense expert witness was successful in educating jurors regarding limitations in the initial expert's conclusions, leading to a greater number of not-guilty verdicts. The judicial instructions were shown to have no impact on verdict decisions. These findings suggest that providing opposing expert witnesses may be an effective safeguard against invalid forensic testimony in criminal trials.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.001 | 0.003 |
| 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