Bibliographic record
Abstract
In Canada (Public Safety and Emergency Preparedness) v Chhina (“Chhina”), a majority of the Supreme Court of Canada found that immigration detainees in detention of lengthy and uncertain duration may access habeas corpus relief at provincial superior courts. The majority held that the detention review scheme under the governing statute did not accord as broad and advantageous protection as that provided by habeas corpus. The author argues that Chhina is a positive development with missed opportunities. The Supreme Court laudably clarified the case law and narrowed the exception that previously barred immigration detainees from seeking any form of habeas corpus relief. Further, in finding that the governing detention review scheme is not as broad and advantageous as habeas corpus, the majority adopted a pragmatic approach and focused on how detention reviews are conducted, not on how they ought to be performed. Notwithstanding these strengths, the majority missed an opportunity to resolve the conflicting case law on three common issues that arise when detainees in lengthy immigration detention file habeas corpus petitions: (i) determining when a detention becomes unduly lengthy such that it is unlawful; (ii) developing a singular approach to calculating the duration of detention; and (iii) establishing the proper weight that should be ascribed to detainee cooperation in habeas corpus applications. Moreover, in omitting substantive discussions of the broader context surrounding immigration detention, the majority missed a valuable opportunity to fully shine the light on the shortcomings of immigration detention and in the process potentially usher in systemic changes.
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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 | 0.009 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.010 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.007 |
| 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".