As Good as it Gets? Security, Asylum, and the Rule of Law after the Certificate Trilogy
Bibliographic record
Abstract
This article uses constitutional discourses on the legality of security certificates to shed light on darker, neglected corners of the security and migration nexus in Canada. I explore how procedures and practices used in the certificate regime have evolved and migrated to analogous adjudicative and discretionary decision-making contexts. I argue, on the one hand, that the executive’s ability to label persons security risks has been subjected to meaningful constraints in the certificate regime and other functionally equivalent adjudicative proceedings. On the other hand, the ability of discretionary decision makers to deport individuals who pose de jure security risks to face torture or similar abuses remains effectively unconstrained—so much so that it is doubtful that Canada has complied with Suresh. If the Supreme Court of Canada takes its own rationale in the certificate trilogy seriously, it must either revise its position in Suresh or encourage the extension of the procedures and practices used in the certificate regime to the entire security and migration nexus, including the removal process.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.030 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".