Canada’s responses to the torture of citizens: Rights aberration or rights aspiration?
Bibliographic record
Abstract
Traditional analyses of Canada’s behaviour on international human rights tend to view it through the prism of the state’s global identity as a good international citizen. Such explanations are limited in helping us understand Canada’s inconsistent responses to allegations that two citizens were tortured in the war on terror—Maher Arar and Omar Khadr. This article uses Jutta Brunnée and Stephen Toope’s interactional account, which emphasizes the need for continuous shared practices of legality in order for international human rights norms to exert influence, to analyze Canada’s responses to the torture of citizens. It argues that to make sense of Canada’s behaviour, we need to examine the role of different state and non-state actors in terms of whether they were agitating for Canada’s compliance with the international prohibition against torture. Civil society was critical in shaping the responses of the Canadian state to the torture of citizens.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.029 | 0.014 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".