A Knee Jerk Reaction: The Removal of Peremptory Challenges in the Canadian Jury System
Bibliographic record
Abstract
The acquittal of Gerald Stanley, a white farmer, in the shooting death of Colten Boushie, a young Cree man, created huge public outcry. It was shared in the news that Stanley was acquitted by an all-white jury after his lawyers used five peremptory challenges to remove the only visibly Indigenous People from the jury pool. This thesis uses a qualitative content analysis to analyze parliamentary debates at the Committee and Senate levels as well as news media sources discussing Bill C-75 and the removal of peremptory challenges in response to the Boushie case. These documents were used to analyze whether the decision to remove peremptory challenges was a well thought out decision based on research and evidence or if it was simply a quick-fix response used by the Liberal government to appear to be doing something about Indigenous underrepresentation in the Canadian Justice System. The analysis shows that many felt the decision to remove peremptory challenges was rushed and fell short of correcting the issue of the underrepresentation of Indigenous people on juries. Expert recommendations were ignored by the government and further work is needed to ensure that Indigenous people are properly represented on juries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.020 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.061 | 0.022 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.006 |
| 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".