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Record W4396617619 · doi:10.1177/14624745241237173

Playing “mental judo”: Mapping staff compassion in Canadian federal prisons

2024· article· en· W4396617619 on OpenAlexafffundabout
Katarina Bogosavljević, Jennifer M. Kilty

Bibliographic record

VenuePunishment & Society · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicEmotional Labor in Professions
Canadian institutionsUniversity of Ottawa
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsCompassionPrisonPsychologySocial psychologyDisgustFeelingMental healthPublic relationsCriminologyPolitical sciencePsychotherapistLaw

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.489
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.034
GPT teacher head0.336
Teacher spread0.302 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2024
Admission routes3
Has abstractyes

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