“Do you want to help or go to war?”: Ethical challenges of critical research in immigration detention in Canada
Bibliographic record
Abstract
In a time of mass displacement, countries across the globe are seeking to protect borders through coercive methods of deterrence such as immigration detention. In Canada, migrants—including children—may be detained in penal facilities having neither been charged nor convicted of crimes. In this paper we examine how we dealt with the series of ethical dilemmas that emerged while doing research in immigration detention centres in Canada. Using a critical ethnographic approach, we examine the process of our research in the field, seeking to understand what our emotional responses and those of the staff could tell us about detention itself, but also about what is at stake when researchers are faced with the suffering of participants in these spaces of confinement. The findings suggest that field work in immigration detention centres is an emotionally demanding process and that there were several pivotal moments in which our sense of moral and clinical obligations toward distressed detainees, especially children, were in conflict with our role as researchers. We also grapple with how the disciplinary gaze of the detention centre affects researchers entering the space. Given these tensions, we argue, spaces of critical reflection that can consider and contain the strongly evoked emotions are crucial, both for researchers, and perhaps more challengingly, for detention centre employees and gatekeepers as well.
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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 | 0.037 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.108 | 0.102 |
| Scholarly communication | 0.023 | 0.007 |
| Open science | 0.005 | 0.015 |
| Research integrity | 0.008 | 0.019 |
| Insufficient payload (model declined to judge) | 0.002 | 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".