Mr. Big and the New Common Law Confessions Rule: Five Years in Review
Bibliographic record
Abstract
The Supreme Court of Canada released its decision of R v Hart in July of 2014. The decision provided a two-prong framework for assessing the admissibility of confessions obtained through the undercover police tactic known as “Mr. Big”. The goal of the framework was to address reliability concerns, to protect suspects from state abuse, and to reduce the risk of wrongful convictions. The first prong of the test created a new common law evidentiary rule, under which Mr. Big obtained confessions are now presumptively inadmissible. The second prong revamped the existing abuse of process doctrine.In this article, the authors review the last five years of judicial application of the new Hart framework. In total, all 61 cases that applied Hart were analyzed qualitatively and quantitatively, looking at whether the goals of the Hart framework have been met, what effect the framework has had on the admissibility of Mr. Big obtained confessions, and what, if any, shortcomings the framework has. The authors argue that the flexibility and discretion built into the Hart framework have resulted in an inconsistent application of the two-prong test. In the end, the framework has had a negligible impact on the number of confessions that are admitted.
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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 | 0.014 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 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".