Playing “mental judo”: Mapping staff compassion in Canadian federal prisons
Bibliographic record
Abstract
Prisons are inherently emotional environments where both staff and prisoners engage in a continuous process of emotion management while working and living in carceral spaces. This paper explores how Correctional Service of Canada (CSC) values and norms shape how predominantly nonuniformed staff manage compassion inside the prison environment. This includes when and to whom they are allowed to express compassion, when they need to hide or suppress the expression of compassion, and how expressing compassion toward prisoners can elicit feelings of disgust among some staff. We argue that, in the emotional arena that is prison, compassion is (re)configured into an individualized and compulsory emotion by way of CSC's organizational emotion culture that emphasizes punishment and control (the security-care nexus) rather than a transformative act that helps to resist the harms of incarceration and encourages healing. Compassion thus becomes a disciplinary apparatus whereby staff self-discipline as they alter their own emotional orientation toward their work, prisoners, and other staff and as a practice to collectively surveil, evaluate, and regulate one another. We contend that compassion bound to questions and practices of security stifles rehabilitation in this environment and that health and other care work must be reintegrated into community settings.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.018 | 0.007 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".