Access to Justice for the Wrongfully Accused in National Security Investigations
Bibliographic record
Abstract
Among the casualties in the ‘war on terror’ is the presumption of innocence. It is now known that four Canadians who were the subject of investigation by the RCMP and CSIS were detained and tortured in Syria on the basis of information that originated in and was shared by Canada. None has ever been charged with a crime. On their return home, all four men called for a process that would expose the truth about the role of Canadian agencies in what happened to them, and ultimately help them clear their names and rebuild their lives. To date, in varying degrees, all four men continue to wait for that “process.” In this paper, I examine the access to justice mechanisms available to persons who are wrongfully accused of being involved in terrorist activities. Utilizing the case study of one of the four men, Abdullah Almalki, I explore the various processes available to him: (i) a complaint to the relevant domestic complaints bodies, the Security Intelligence Review Committee and the Commission for Public Complaints Against the RCMP; (ii) a commission of inquiry; and (iii) a civil tort claim. Due in large part to the role national security confidentiality plays in these mechanisms, all three models are found to be ineffective for those seeking accountability in the national security context.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.016 | 0.017 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".