"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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".