Bibliographic record
Abstract
“Rendition” is the United States’ policy of sending terrorism suspects to be interrogated in Middle Eastern countries that practice torture.\nThis Article introduces the subject by describing a complaint filed in a lawsuit by Canadian citizen Maher Arar. The United States sent Arar from John F. Kennedy airport to Syria, where he was tortured and was held in a grave-sized cell for nearly a year. Arar alleges that his transfer violated the Convention Against Torture and Other Cruel, Inhuman or Degrading Treatment or Punishment (“CAT”).\nArar’s lawsuit may be dismissed before the court reaches the substance of his claims. But much of the evidence needed to evaluate his charges is already a matter of public record. This Article attempts to compile that evidence, which has been reported in hundreds of different sources, into single coherent account for the first time.\nThis factual information, important in its own right, is also crucial in addressing the Bush administration’s most common defense of rendition: the argument that before every rendition it obtains diplomatic assurances that a suspect will not be tortured, and these are enough to reduce the odds of torture to under 50 percent and comply with Article 3 of the CAT.\nArticle 3 requires the administration to consider all evidence relevant to the danger of torture before transferring a prisoner. A thorough review of this evidence demonstrates that the odds of torture after a rendition are far greater than fifty percent, and diplomatic assurances do virtually nothing to reduce those odds.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.028 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".