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Record W4283362371 · doi:10.1177/17488958221105825

Invisible ghosts of care and penality: Exploring Canadian correctional workers’ perceptions of prisoner well-being, accountability and power

2022· article· en· W4283362371 on OpenAlexaffabout
Matthew S. Johnston, Rosemary Ricciardelli

Bibliographic record

VenueCriminology & Criminal Justice · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsAccountabilityPrisonPunitive damagesMainstreamCriminologyPower (physics)Mental healthNegotiationPolitical scienceSociologyPsychologyPublic relationsLawPsychiatry

Abstract

fetched live from OpenAlex

Much correctional work is generally misunderstood by the mainstream media and many public circles as solely punitive and authoritative, which has fueled many politicized outcomes for correctional policy, practice and intervention. Reasonably, critical criminological discourse is steered primarily by the perspectives and voices of prisoners and victims. Yet this privileging leaves many questions remaining about how correctional workers in the contemporary era negotiate their complex duties of both prisoner care and accountability. Drawing on data garnered from open-ended survey responses of provincial and territorial correctional employees (n = 876) in Canada, we explore how Canadian correctional workers balance their emotional and occupational framework and perspectives with integrity. Informed through a lens of emotional labour, we find that many Canadian correctional workers recognize the need for, and gap in, prisoner care, mental health and rehabilitation, while also problematizing the shift and decline in prisoner accountability, which they believe jeopardizes both correctional worker and prisoner safety. We discuss the implications our findings present in relation to questions of power and control in prison spaces.

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, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.132
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.056
GPT teacher head0.319
Teacher spread0.263 · 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 designQualitative
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

Citations17
Published2022
Admission routes2
Has abstractyes

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