Interrogating the operation of empathy in social work with noncitizens
Bibliographic record
Abstract
Based on interviews I conducted with social workers in Canada, this article offers a critique of empathy as a foundation of good social work. More specifically, I examine how empathic feelings produce the social worker as a knowing, moral and innocent subject in their work with noncitizens. Drawing on critical theories of affect and emotions that reconceptualise feelings as social practice, I examine how empathy facilitates proximity with and knowledge production about noncitizens among social workers. I attend to various historical lines of empathic feeling among differently positioned social workers and trace the concrete ways in which the feeling of empathy circulates and ‘sticks’, as social workers navigate exclusionary practices towards noncitizens. I argue that empathy, while imagined as an affective entry to minimising the professional–client distance, could instead function to secure social workers’ sense of innocence and morality, confirming their professional identity as facilitated by the script of whiteness.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.027 | 0.027 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| 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".