Stories From The Hidden World Of Immigration Detention In Canada: A Phenomenological-Narrative Study
Bibliographic record
Abstract
This study presents a phenomenological-narrative inquiry of the lived experience of individuals who have been detained in Canada's immigration detention system. The inhumane treatment within immigration detention remains largely hidden in Canadian media and discourse as it is overshadowed by Canada's global reputation as a benevolent immigrant nation-state. Semi-structured interviews have been conducted with two former detainees in Toronto, Ontario to extract stories of the lived experiences of detainees on their own accounts. The interviews covered the lived experiences of detainees before, during, and after immigration detention. Findings are presented in themes whereby direct statements from participant interviews are centred to provide authenticity in presenting their stories. The interviews are analyzed alongside academic literature with crimmigration and structuration as guiding theoretical frameworks. I argue that while participants' autonomy was curtailed due to the inhumane treatment and carceral settings they were subjected to, they were able to continue exercising their agency through their efforts to organize their release, their everyday interactions, and how they choose to make sense of their realities.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.044 | 0.025 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".