Incalculable Harm: Analyzing the Impact of the COVID-19 Pandemic on Immigration Detention in Canada
Bibliographic record
Abstract
This paper reflects on the impact of the COVID‑19 pandemic on immigration detention in Canada. Drawing on research spanning 2020 to 2022, we analyze how the pandemic impacted rates of detention, conditions of detention, and other related issues. Data released by the Canada Border Services Agency shows that despite an initial decrease in absolute numbers, Canada detained people at a higher rate after the onset of the pandemic than it did prior. Canada also held people for longer periods of time and relied more heavily on jails than dedicated Immigration Holding Centres. Conditions of confinement deteriorated significantly across all detention facilities, but most acutely in jails. The abrupt shift towards conducting detention review hearings exclusively by remote means, and initially only by telephone—without ensuring meaningful contact between detainees and their counsel—further impeded detainees’ ability to understand and participate in their own hearings. These factors, combined with increased isolation within jails and detention facilities, increased use of segregation, diminished availability of alternatives to detention, the continued detention of children and separation of families, and the persistence of structural racism and disregard for detainee mental health paint a very grim picture. This research drives us towards the conclusion that the COVID‑19 pandemic has had a devastating impact on immigration detention in Canada. Rather than drive the immigration detention regime towards greater rates of release, as early researchers hoped, the pandemic ushered in an increased reliance on detention under worse conditions, as well as greater alienation, degradation, and dehumanization of detainees. We conclude our analysis by identifying key criteria that must be prioritized to avoid further entrenching the worst of the COVID‑19 era practices and call for the gradual abolition of immigration detention in Canada.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".