Jury Decision Rules in Criminal Trials
Bibliographic record
Abstract
Discussions of juries in democratic systems often frame the jury as a symbol of democracy, an essential safeguard for an accused, and a legitimizer of state authority. However, the context in which the jury operates has evolved, and there is a widening gap between our empirical understanding of juries and our commitment to their theoretical value. Piecemeal reforms of the jury have also moved the system away from the historical model, with the consequence of undermining the remaining aspects. This paper argues one such aspect is the decision rule under which a jury renders its verdict. Canada is one of the last jurisdictions requiring juror unanimity in the criminal context. The United States of America had previously permitted nonunanimous juries but more recently declared nonunanimous verdicts unconstitutional. By contrast, in England and Wales, the birthplace of the common law jury, Parliament legislated supermajorities in 1967 and has not returned to a unanimous decision rule. This paper comparatively analyzes the evolution of the jury decision rule across these three jurisdictions to better understand whether the requirement for unanimous juries is still essential in Canada’s criminal justice system. In today’s jury context and given the content of the decision made by the jury, unanimity appears to be largely symbolic.
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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 | 0.061 | 0.168 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".