Bibliographic record
Abstract
Universally condemned and everywhere illegal, torture goes on in democracies as well as in dictatorships. Nonetheless, many Americans were surprised following the attacks of 9/11 at how easily the United States embraced torture as well as the supposedly lesser evil of cruel, inhuman, and degrading treatment. Nothing seemed extreme when it came to questioning real and imagined terrorists. Extraordinary rendition—sending people captured in the “war on terror” to nations long counted among the world’s worst human rights violators—hid from the public eye cruel and bloody interrogations. “Torture lite” or “torture without marks” became the norm for those in American custody. In Rendition to Torture , Alan W. Clarke explains how the United States adopted torture as a matter of official policy; how and why it turned to extraordinary rendition as a way to outsource more extreme, mutilating forms of torture; and outlines the steps the United States took to hide its abuses. Many adverse consequences attended American use of torture. False information gleaned from torture was used to justify the Iraq war, adding potency to the charge that the war was illegal under international law. Moreover, European nations and Canada aided, abetted, and became thoroughly enmeshed in U.S.-led torture and renditions, thereby spreading both the problem and the blame for this practice. Clarke offers an extended critique of these activities, placing them in historical and legal context as well as in transnational and comparative perspective.
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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 | 0.002 | 0.006 |
| 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 | 0.030 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".