Social Identity in the Canadian Courtroom: Effects of Juror and Defendant Race
Bibliographic record
Abstract
The purpose of this study was to examine whether black (n = 90), Indigenous (n = 92), and white (n = 94) mock jurors would make harsher decisions in trials involving other-race defendants. Jury-eligible community members recruited via Qualtrics read a fictional impaired driving and dangerous operation of a motor vehicle case in which the defendant’s race varied (black, Indigenous, white). They then made verdict/sentencing decisions and completed measures of stereotypes. We predicted that mock jurors who endorsed negative racial stereotypes would be more likely to vote guilty and recommend harsher sentences for other-race defendants. Instead, we found that positive personally held stereotypes predicted leniency among white jurors judging Indigenous defendants but no such effects for other trial party combinations. Overall, the black defendant received significantly more lenient decisions as compared to the white defendant. Although no formal policy ensures that specific groups are represented on juries, these data indicate that people process trial information differently as a joint function of juror and defendant race.
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".