A Bandage on A Broken System: Moving Beyond Peremptory Challenges To Increase Indigenous Juror Representation In Canada
Bibliographic record
Abstract
In 2016, Colten Boushie, a 22-year-old Indigenous man, was fatally shot by Gerald Stanley, a white farmer. Stanley was later acquitted of second-degree murder and manslaughter by an all-white jury. Peremptory challenges became the major legal focus, with the all-white jury attributed to the defense attorney’s peremptory dismissal of five Indigenous individuals from the final jury panel. Following a raucous public debate, just two months after Stanley’s acquittal, Canada’s Government quickly introduced Bill C-75, eliminating peremptory challenges. While some legal actors view the ban on peremptory challenges as a step toward improving Indigenous juror participation, others argue that this elimination decreases Indigenous representation. As the insular debate endures, it continues to distract from numerous substantial issues with more profound implications on Indigenous juror representation. Through an analysis of the Jury Acts of Ontario, Saskatchewan, and Manitoba, this Article highlights how provincial jury pool selection and summoning policies continue to encourage Indigenous exclusion. For more representative juries, Canada must move past peremptory challenges and acknowledge that sustained efforts made in partnership with Indigenous communities are desperately needed. Examples are offered of structurally-oriented, deeper reform actions to begin the process of addressing root causes of white-washed criminal juries 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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.039 | 0.008 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".