Review and Oversight of National Security Activities With Some Reflections on Canada's Arar Inquiry
Bibliographic record
Abstract
The concluding chapter of Philip B. Heymann's and Juliette N. Kayyem's Protecting Liberty in the Age of Terror is devoted to the very important subject of oversight of extraordinary measures. This chapter contains several important insights with respect to review and oversight of the state's increased national security activities in the post-9/11 era. First, the authors argue that oversight should be concerned not only with the fairness of extraordinary measures, but their effectiveness. Second, they argue that that the task of oversight must be divided between both the legislature and the executive and that internal checks within the executive branch ought to be increased. Drawing on these insights, the authors conclude with three main recommendations: (1) that a nonpartisan commission be created to provide for continuing annual and comprehensive reviews of anti-terrorism measures that have significant effects on rights; (2) that Inspectors General within the executive be required to provide systemic reviews of the extraordinary powers exercised by their respective agencies; and (3) that an interagency committee of Inspectors General be formed to conduct joint reviews of agencies that use different extraordinary powers for a shared purpose. In this comment, I will argue that many of Professor Heymann's and Kayyem's insights about the importance and challenges of oversight of the state's increased national security activities are correct and should be heeded. Review is a task for all branches of government including the independent judiciary, specialists in the executive branch and generalists in the legislative branch. It is also important for review bodies to share information, be subject to co-ordination and conduct joint reviews. Co-ordinated review is necessary to mirror and match increased linkages between various agencies with national security mandates and the creation of a whole government approach to the prevention of terrorism. The expansion of national security activities requires new institutions to ensure effective review and public confidence in what are often secret activities. I also explore the relation between review to determine the propriety and the efficacy of the state's security strategies.
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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 | 0.025 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.022 | 0.014 |
| Scholarly communication | 0.023 | 0.007 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.018 | 0.013 |
| 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".