"I didn't feel like a human in there" Immigration detention in Canada and its impact on mental health
Bibliographic record
Abstract
Despite its reputation as a refugee-welcoming and multicultural country, Canada incarcerates thousands of people on immigration-related grounds every year, including people who are fleeing persecution, those seeking employment and a better life, and people who have lived in Canada since childhood. Immigration detainees are held for noncriminal purposes but endure some of the most restrictive conditions of confinement in the country, including maximum security jails and solitary confinement, with no set release date. Figures from the Canada Border Services Agency (CBSA) reveal that the number of immigration detainees incarcerated in Canada has increased every fiscal year between 2016-17 and 2019-20, peaking in fiscal year 2019-20 with a total of 8,825 people in immigration detention. Since the onset of the Covid-19 pandemic in March 2020, Canadian authorities have released immigration detainees at unprecedented rates, providing clear evidence that there are viable alternatives to depriving people of their liberty for indeterminate periods of time. For many of those who remained incarcerated, conditions of detention became harsher, with far more frequent lockdowns and limited access to phones and showers. During the first year of the pandemic, immigration detainees went on hunger strike three times at the Montreal-area immigration holding center.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.019 | 0.005 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".