Extraordinary Rendition, the Canadian Edition: National Security and Challenges to the Global Ban on Torture
Bibliographic record
Abstract
Since the September 11th terrorist attacks in the United States, concerns that counter-terrorism laws and practices are directly and indirectly causing human rights violations have mounted. Much of the international focus has been on the United States. This paper highlights, however, that it is a debate with a truly global dimension. The author examines particular concerns that Canadian law enforcement and security agencies may have been complicit in the imprisonment and torture abroad of Canadian citizens who were of interest in the context of national security investigations. The cases are situated in the wider debate about torture that has emerged worldwide in recent years, and argues that any move to create exceptions to the absolute prohibition of torture, enshrined in numerous international treaties, would lead to both injustice and insecurity.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.012 | 0.010 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".