Examining the judicial decision to substitute credibility instructions for expert testimony on confessions
Bibliographic record
Abstract
Purpose The present study tested the judicial decision to deny false confession expert testimony on the basis that jury instructions are sufficient to aid jurors in their determinations of disputed confession evidence. Methods Three groups of mock jurors ( N = 150) were presented with a trial summary that included a videotaped re‐enactment of an interrogation in which the interrogator used a maximization ploy. One group received expert testimony in the trial summary, another group received credibility instructions, and a control group received neither. All participants received standard reasonable doubt instructions at the end of the trial summary and then answered questions such as their verdict in the case, the defendant's likelihood of guilt, and the voluntariness of the defendant's confession. Results The results showed a high rate of conviction that was only reduced when participants received expert testimony. Across all measures, no significant differences were found between the control and credibility instruction groups. Conclusions The results suggest that credibility instructions are not comparable to expert testimony in influencing jurors' judgments of disputed confession evidence. These findings do not support the judicial decision to deny expert testimony on the basis that credibility instructions alone are sufficient to aid potential jurors in their evaluations of confession evidence. Avenues for future research on expert testimony and jury instructions in confession cases 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 | 0.017 | 0.207 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".