Secret Evidence and the Due Process of Terrorist Detentions
Bibliographic record
Abstract
Courts across many common law democracies have been wrestling with a shared predicament: proving cases against suspected terrorists in detention hearings requires governments to protect sensitive classified information about intelligence sources and methods, but withholding evidence from suspects threatens fairness and contradicts a basic tenet of adversarial process. This Article examines several models for resolving this problem, including the "special advocate" model employed by Britain and Canada, and the 'Judicial management" model employed in Israel. This analysis shows how the very different approaches adopted even among democracies sharing common legal foundations reflect varying understandings of 'fundamental fairness" or "due process," and their effectiveness in each system depends on the special institutional features of each national court system. This Article examines the secret evidence dilemma in a manner relevant to forseeable reforms in the United States, as courts and Congress wrestle with questions left open by Boumediene v. Bush.
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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 | 0.068 | 0.189 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.025 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.014 | 0.012 |
| 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".