Secret Evidence in Civil Litigation against the Government
Bibliographic record
Abstract
UN Security Council Resolution 1373 is rightly viewed as the most significant international instrument pertaining to countering terrorism. Two features of the resolution are of concern to us: the sharing of security intelligence and other information, and, restrictions to human mobility. These two dimensions of global counterterrorism dovetail within the dark underbelly of the securitization of migration. There have been several high-profile instances where the United Kingdom and Canada have been directly involved in deportation to torture. Victims have faced numerous obstacles securing remedies for human rights violations. Perhaps most significant is the unwillingness of states to disclose relevant information during civil litigation, owing to national security or more broadly public interest privilege. The United Kingdom has approached this problem by instituting “Closed Material Proceedings” (CMPs), where courts may base decisions on secret evidence, with the benefit of security-cleared Special Advocates mandated to represent the interests of plaintiffs. Canadian authorities have considered similar measures, having a rich history of secret hearings. Clearly, the use of secret evidence raises serious rule of law issues, as well as many practical, professional, and ethical challenges. This chapter explores these issues in the context of civil litigation of state responsibility for torture and other human rights abuses. As part of an ongoing socio-legal study on CMP in Canada and the UK, this chapter presents documentary and empirical findings, including interviews with judges, Special Advocates, and court administrators in both jurisdictions.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.016 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.013 | 0.009 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".