The Common Law Confessions Rule in the Charter Era: Current Law and Future Directions
Bibliographic record
Abstract
In an era of Charter protections, the common law rule excluding involuntary confessions remains a suspect’s best legal protection against coercive interrogation. This paper reviews and evaluates the current Canadian confessions rule and points to some ways the protection it offers might be strengthened. The basic contours of the rule are drawn from R. v. Oickle, the leading Supreme Court of Canada case, and refinements to the rule from the recent Supreme Court cases of R. v. Spencer and R. v. Singh are also discussed. The paper concludes that the confessions rule represents a modest but crucial safeguard for the accused that pursues the two distinct goals of excluding unreliable evidence and ensuring fair treatment of interrogated suspects. It is argued that the rule is better suited to promote the first of these two goals, since it reflects insights from the social science literature on false confessions but places only indirect restraints on interrogators. In any event, the need to balance suspects’ procedural protections against the exigencies of law enforcement places unavoidable limits on the rule’s capacity to advance either goal. The paper suggests several ways in which lawmakers might fortify the protection offered by the confessions rule: by imposing clearer limits on police practices, by offering further guidance on the treatment of vulnerable suspects, by encouraging police to record interrogations, by looking to other Charter rights as safeguards against coercive interrogation, and by educating juries about the problem of false confessions. Acknowledgment: This paper draws on my dissertation, the Problem of Jury Error in Canadian Criminal Evidence Law, which was completed in fulfillment of the requirements for the J.S.D. degree from Yale University. I gratefully acknowledge the institutional support of Yale Law School, and of the Social Sciences and Humanities Research Council of Canada, which funded the research with a Doctoral Fellowship.
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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 | 0.029 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.008 | 0.060 |
| Scholarly communication | 0.019 | 0.023 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.017 | 0.018 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".