The role of defendant race, expert testimony and interrogation coerciveness on Canadian mock jurors' perceptions of recanted confessions
Bibliographic record
Abstract
Abstract Purpose In some contexts, US‐based White jurors appear to exhibit a heightened focus on legally relevant information when the defendant is Black as compared to White. The current study tested this ‘watchdog’ effect in the Canadian context by examining mock jurors' decisions using a trial involving a recanted confession with an Indigenous or a White defendant. We also aimed to enhance our understanding of whether expert testimony sensitizes jurors to issues surrounding confessions, or if such testimony engenders a general scepticism towards this form of evidence. Methods White Canadian jury‐eligible community members ( N = 762) read a trial transcript in which we manipulated interrogation coerciveness (high/low), expert testimony (present/absent) and defendant race (Indigenous/White), and then rendered a verdict and responded to a questionnaire about the trial. Results Mock jurors were significantly less likely to convict the defendant in the high as compared to low coercion conditions. Expert testimony had no significant effects on verdict or perceptions of the confession. Participants seemed more sceptical of the Indigenous defendant's confession, perceiving it to be less truthful (only in high‐coercion conditions, providing the only evidence of the ‘watchdog’ effect in this study), voluntary and honest than that of the White defendant. However, defendant race had no effect on the verdict. Conclusions Participants were sensitive to differences between high and low coercions without the assistance of expert testimony. Furthermore, our results concerning the Indigenous defendant suggest participants' verdicts were influenced by factors beyond their perceptions of the confession evidence.
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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 | 0.008 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".