Bibliographic record
Abstract
At common law, the privilege against self-incrimination protects the accused solely against compelled testimony in formal proceedings. Under the Canadian Charter of Rights and Freedoms, the privilege received constitutional protection and a pre-trial right against self-incrimination was subsequently created. The Supreme Court of Canada (SCC) stated in the early 1990s that the principle against self-incrimination is a principle of fundamental justice under s. 7, “perhaps the single, most important organizing principle of criminal justice”. This allowed for the protection of the accused against compelled confessions outside of the narrow context of formal proceedings.Despite the significant developments of the protection against self incrimination, limitations to its application remain. The present chapter will address one of these limits: the exclusion of individuals subjected to Mr. Big undercover operations from the protection of the right against self incrimination under s. 7, on the basis that its application is limited to confessions obtained while the individual was in state detention. In order to assess whether a legal protection should apply to a certain situation, one ought to look at the rationales for which the protection exists and how that pairs with the situations at issue. I will argue that, despite the suspect not being in physical state detention, the very basis on which Mr. Big operations function violates all rationales behind the right against self-incrimination.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".