From Cooperation, to Complicity, to Compensation: The War on Terror, Extraordinary Rendition, and the Cost of Torture
Bibliographic record
Abstract
Abstract Attention has turned recently to the human rights implications of Western states' cooperation with the United States in the so-called War on Terror. This paper presents the ordeal of Canadian Maher Arar as a case-study in how one state responded to contentions of complicity in the extraordinary rendition of one of its nationals. Relying in part on faulty intelligence supplied by Canada, Arar was rendered by the United States to Syria. He was imprisoned and tortured for almost a year before Canada secured his release. Under considerable public pressure, the Canadian government appointed an independent public inquiry to examine the events surrounding his rendition. Following the release of the report and its recommendations, the Canadian government formally apologized to Arar and paid him substantial compensation. The author provides an account of the function performed by independent public inquiries in responding to public calls for government accountability in face of alleged wrongdoing. The paper describes the challenge posed by competing demands for publicity and secrecy in the particular context of controversial actions taken in the name of national security. Finally the author considers the precedential value of the Arar Inquiry for other jurisdictions that face similar allegations regarding complicity in human rights violations, as well as the task of devising a fair and reasonably open process against claims of national security confidentiality.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.042 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".