Revisiting Jury Instructions on Racial Prejudice Towards Indigenous Peoples in Criminal Jury Trials
Why this work is in the frame
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Bibliographic record
Abstract
<p>This article examines the Supreme Court of Canada’s assumptions in Barton and Chouhan on racial bias in Canadian criminal jury trials. Jury research offers important insights related to the differential impact of jury instructions for racialized and Indigenous persons, and for accused and victims. If jurors cannot understand jury instructions, or if jury instructions, or victim or defendant race, do not predict sentencing or conviction outcomes, then we might have little confidence in reducing anti-Indigenous prejudice through jury instructions. Worse yet, if jury instructions prime, rather than suppress, prejudicial reasoning, then we may want to entirely rethink the use of specialized instructions for this purpose; our reforms might instead focus on jury diversification. I argue that the Canadian jury research casts doubt on the Supreme Court of Canada’s jurisprudence on a juror’s capacity to control racial bias against Indigenous persons in criminal trials.</p>
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it