Mosaic or Melting Pot? Race and Juror Decision Making in Canada and the United States
Bibliographic record
Abstract
Although Canada and the United States both demonstrate significant overrepresentation of racialized groups in prisons, the overrepresented groups vary by country, potentially signifying results of the countries’ different (though similarly problematic) histories of racial inequality. The present study investigated this issue within a jury context by assessing the influence of defendant race on Canadian and American participants’ verdicts in an assault trial. We also examined mock jurors’ attributions of the defendant’s behavior and their perceptions of the cultural criminal stereotype for each racial group. Canadian and American participants ( N = 198) read a trial transcript in which the defendant’s race (i.e., Black, White, or Aboriginal Canadian/Native American) was manipulated, and then completed measures of attributions and stereotypes. Results demonstrated that although verdicts did not significantly differ as a function of defendant race or country, stability and control attributions did vary between Canadian and American participants, as did racial stereotypes. In addition, defendant race affected internal versus external attributions, regardless of country. These findings suggest that race may play a role in jurors’ perceptions of defendants, but that in some ways, this varies by country, potentially accounting for some of the differences found between existing Canadian and American jury studies.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".