The Role of Defendant Race and Racially Charged Media in Canadian Mock Juror Decision Making
Bibliographic record
Abstract
This study examined the influence of defendant race and race salience (manipulated via racially charged media) on Canadian mock jurors' judgements. Two hundred ten jury-eligible Canadian online participants read a racially charged (general or specific to the defendant's race) or neutral article followed by a trial transcript that involved dangerous operation of a motor vehicle and impaired driving charges against a White, Black, or Indigenous defendant. Diverging from previous findings, this study did not find effects of defendant race or race salience on verdict judgements or causal attributions. However, when race is not a central feature of the case, making race salient outside the trial may increase levels of racial bias for some mock jurors. When the defendant was Black, a race-specific article appeared to backfire, producing the harshest sentencing recommendation compared to race-neutral and general race articles. Conversely, for the Indigenous defendant, any mention of race produced harsher recommended sentences relative to no mention of race. Results do not seem to parallel those found in U.S. race-salience studies. Rather, this specific race-salience technique may be detrimental to a minority defendant's case in a Canadian context.
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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 | 0.005 | 0.066 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| 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".