The limits of police deception in obtaining a confession from a suspect who is neither arrested nor detained : the Canadian Supreme Court leads the way
Bibliographic record
Abstract
In Canada, confessions are sometimes obtained through what is commonly known as ‘Mr Big’ operations. These involve recruiting a suspect into a fictitious criminal organisation with a view to obtaining a confession from him or her. Because of the unique circumstances under which such confessions are made, there is real danger of abuse of power by the police and of unreliable confessions. The suspect is unaware of the status of the person hearing the confession and no constitutional warnings are necessary. This practice provides an opportunity to view police deception from a different angle. Because of the central role played by the police in obtaining these confessions, and because even reliable confessions cannot be admissible in the face of improper police conduct, it is submitted that the reliability of such confessions and the manner in which they were obtained should be considered together when judging their admissibility.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".