Hart and Mack: New Restraints on Mr. Big and a New Approach to Unreliable Prosecution Evidence
Bibliographic record
Abstract
Taken together, the Supreme Court of Canada’s recent judgments in R. v. Hart and R. v. Mack represent a coherent approach to confessions arising from Mr. Big operations. The Court has now recognized that these operations carry risks of generating evidence that is both unreliable and prejudicial, and of becoming abusive. Hart and Mack erect some safeguards for the accused in the Mr. Big context. The judgments should encourage police to exercise restraint in using the technique and courts to be more vigilant in assessing the resulting confessions. Even outside the Mr. Big context, the judgments may be relied on in future cases to place some limits on undercover operations. Finally, it is argued that the Court’s approach to the reliability problems of Mr. Big confessions carries the potential to enhance protections against wrongful convictions based on other forms of unreliable evidence. Hart can be read as recognizing trial judges’ discretion to exclude unreliable evidence, while Hart and Mack together suggest that both an exclusionary rule and a rule requiring cautionary jury instructions may be needed to respond to serious concerns about the reliability of prosecution evidence.
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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 | 0.036 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.009 | 0.049 |
| Scholarly communication | 0.017 | 0.015 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.024 | 0.032 |
| 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".