Everyday Injustices: Barriers to Access to Justice for Immigration Detainees in Canada
Bibliographic record
Abstract
The growing Canadian immigration detention system touches upon the lives of thousands of people daily. However, despite significant legal and normative problems, the Canadian detention system seems to be escaping sustained scrutiny. To address this gap, we employ the rubric of “access to justice” to refocus on inequalities being reproduced in the legal system that impede fair, unprejudiced, and non-arbitrary treatment for minorities and vulnerable people. If law is meant to govern equally and to ensure against arbitrary deprivations of liberty, immigration detainees should not be placed outside its reaches. Yet, our examination of access to justice in the Canadian detention system demonstrates that exactly this sort of displacement is occurring. Above and beyond the basic deprivation of liberty and setback to immigrants and asylum-seekers’ interests, detention inflicts irreparable psychological, physical, and social damage. We point to issues such as deteriorating daily detention conditions, far-flung facilities locations, unfair discretionary decision-making, lack of options for women, children, and vulnerable people, the compounding reasons for indefinite detention, and inadequate legal aid and access to counsel. Canada is propagating an extremely costly and ineffective system of administrative detention that is often in contravention of national and international standards on immigration detention.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.030 | 0.007 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".