“The mother seems to traumatize her child”: Examining empathy, denial, and responsibility in day-to-day encounters of families and staff in immigration detention in Canada
Bibliographic record
Abstract
This paper examines encounters of mothers and their children with detention facility staff during our fieldwork in immigration detention centres in Canada. We sought to understand how detainees and institutional staff understand each other and their roles within the broader system. Using a critical ethnographic frame that views the inner psychic worlds of subjects as contingent upon larger systems of power and oppression we organize our data around narrative and content themes. Our findings suggest that guards and staff see their roles as protectors of children, even as they communicate implicitly that these families are risks . Further, we propose that staff tend to project the aggressor onto the Other, in this case, migrant mothers, as a way to cope with the moral distress of witnessing the suffering of detained children, and with the burden of potential complicity. By describing how empathy, denial and responsibility are negotiated in these custodial spaces, we analyze the ways these micropolitical encounters can illuminate larger trends in the representation and reception of migrants with important implications for mental health care and border control practices and policy more broadly. • Children often elicited feelings of empathy and care in detention centre staff. • Staff hoped to be seen as providing protective care but also implied that detainees were dangerous. • Staff experienced moral distress and used projection and scapegoating as a defence. • Policies and training must account for these face-to-face dynamics in detention.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.035 | 0.019 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.011 |
| 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".