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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.025 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.026 | 0.054 |
| Scholarly communication | 0.021 | 0.009 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.010 | 0.015 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".