<i>R v Hart</i>: A New Common Law Confession Rule for Undercover Operations
Bibliographic record
Abstract
The Supreme Court of Canada's recent decision in R v Hart will be of interest to judges and criminal lawyers throughout the commonwealth. At issue in the case was the question of whether an accused could challenge the admissibility of a confession given in the course of ‘Mr. Big’ undercover police investigation (an elaborate undercover police investigations in which a suspect is misled into believing that he or she is being recruited into a fictitious criminal organisation.) In Hart, Canada's apex court broke sharply with its previous jurisprudence, creating a new, situation specific, common law rule of evidence. Henceforth, confessions obtained in the course of Mr. Big operations are presumptively inadmissible unless the Crown can prove the confession is reliable on a balance of probabilities. In this commentary, the authors argue that, while Hart is a welcome development in the law, commonwealth courts should be cautious in following the Supreme Court of Canada's approach. In the author's view, a better approach would be to modify the existing common law confession rule so that it applied to Mr. Big operations. Such an approach would produce greater certainty in the law and would afford greater protection for the accused.
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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 | 0.016 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.018 |
| Scholarly communication | 0.017 | 0.006 |
| Open science | 0.009 | 0.004 |
| Research integrity | 0.025 | 0.023 |
| Insufficient payload (model declined to judge) | 0.003 | 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".