Police Questioning in the Charter Era: Adjudicative versus Regulatory Rule-making and the Problem of False Confes-sions
Bibliographic record
Abstract
Unlike in the areas of detention and search, Parliament has played no role in regulating the questioning of adult criminal suspects by police. This paper examines the implications of this legislative silence. Critics of the courts’ use of the ancillary powers doctrine in the law of detention and search have argued that the optimal regulation of police investigative practices requires robust legislative input. I argue that the same is true of police questioning. But given the improbability that this will happen, I argue that appellate courts should adopt a more robustly “regulatory” (as opposed to “adjudicative”) approach to both the common law confessions rule and section 10(b) of the Charter. I then explore how such an approach could better address the chief policy issue raised by police questioning: false confessions.
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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 | 0.212 | 0.301 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.009 | 0.073 |
| Scholarly communication | 0.037 | 0.024 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.029 | 0.030 |
| Insufficient payload (model declined to judge) | 0.002 | 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".