Touching Torture with a Ten-Foot Pole: The Legality of Canada’s Approach to National Security Information Sharing with Human Rights-Abusing States
Bibliographic record
Abstract
In 2011, then-Public Safety Minister Vic Toews issued “ministerial directions” to Canada’s key security and intelligence agencies on “Information Sharing with Foreign Entities.” These directions permit information sharing in exigent circumstances, even where there is substantial risk of mistreatment of an individual. After a brief chorus of condemnation, the directions sank into relative obscurity while remaining part of Canada’s national security policy framework. This article aims to reignite discussion of these policies and their controversial content, relying in large measure on documents obtained by the author directly or through journalistic researchers under access to information law. First, I examine dilemmas raised when information is shared between human rights-observing and -abusing states and then focus on the legal parameters and policy context in which both “in-bound” and “out-bound” information sharing takes place. Next, I analyze the 2011 instruments and consider their legality under both international and domestic law. I conclude that the legality of these measures is doubtful in international law—at least in so far as out-bound information sharing is concerned—and that domestic criminal culpability and constitutional validity are very close questions.
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.046 | 0.053 |
| Scholarly communication | 0.022 | 0.007 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 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".