The Influence of Witness Intoxication, Witness Race, and Defendant Race on Mock Juror Decision Making
Bibliographic record
Abstract
Negative stereotypes about Indigenous people concerning alcoholism and criminality permeate Canadian society. This study primarily explores whether racial bias affects mock jurors' perceptions of Indigenous eyewitnesses, particularly when the eyewitness was intoxicated at the time of the crime. Participants read a trial transcript in which eyewitness intoxication and both eyewitness and defendant race (Indigenous/white) were manipulated, then provided a verdict and responded to a series of questions about the eyewitness. We found an indirect effect of eyewitness intoxication on verdict, operating through perceived eyewitness accuracy, such that intoxicated eyewitnesses were associated with significantly fewer convictions. Participants also rated Indigenous eyewitnesses as more accurate than white eyewitnesses. Although there were no significant main effects of defendant or eyewitness race on verdicts, we did observe a significant indirect effect of eyewitness race: Indigenous eyewitnesses were associated with more convictions via perceived accuracy. These effects run contrary to some previous literature and, coupled with our findings regarding criminality stereotypes, suggest that prospective jurors may be becoming aware of systemic bias facing Indigenous peoples. This study adds to the growing body of research investigating prospective jurors' decision making in Canada.
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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 | 0.003 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".