The Jury Vetting Cases: New Insights on Jury Trials in Criminal Cases?
Bibliographic record
Abstract
In this paper the author discusses the Supreme Court of Canada’s decisions in the jury vetting cases of R. v. Yumnu, R. v. Emms and R. v. Davey. The author suggests that while the Supreme Court’s ruling goes a long way toward eliminating the concerns associated with jury vetting, there is a disconnect between the Court’s description of the jury selection process and how counsel tend to think about jury selection in criminal trials. While counsel are limited in their ability to influence the jury selection process, the Court might nevertheless have considered whether a full ban on jury vetting was needed to combat the risk — both real and perceived — that the Crown might act unethically during the jury selection process. The paper also examines whether the Court’s comments about the essential and inalienable features of the jury contribute to our understanding of the right to trial by jury enshrined in section 11(f) of the Canadian Charter of Rights and Freedoms.
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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 | 0.027 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.007 | 0.039 |
| Scholarly communication | 0.018 | 0.028 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.015 | 0.011 |
| Insufficient payload (model declined to judge) | 0.008 | 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".