The next jailor: An empirical study of danger to the public immigration detentions in Canada (summer 2021)
Bibliographic record
Abstract
This article investigates who counts as dangerous for immigration control purposes and how states spot and monitor purportedly dangerous people for immigration enforcement measures. By examining Canadian immigration detention law and practice during the early months of the COVID-19 pandemic, the study finds that nearly all danger-based immigration detentions targeted long-term Canadian residents who had typically lost permanent legal status following a criminal conviction. The article argues that a core function of immigration enforcement processes is the removal of supposedly undesirable persons from society and that danger-based detentions are used primarily for post-admission migration control. Furthermore, the study reveals that the surveillance and policing of dangerous individuals largely relies on external police agencies, with immigration officials rarely initiating or managing their own investigations. The findings from this research contribute to the growing body of literature on the overlap between criminal law and immigration law and shows that—in the context of danger-based immigration detention—immigration authorities do not initiate their own investigations, but depend almost exclusively on the work of criminal police forces.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.032 | 0.007 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 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".