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Record W2789730033 · doi:10.1177/0734016817752433

Correctional Officers in Canada

2018· article· en· W2789730033 on OpenAlexafffundabout
Rosemary Ricciardelli, Nicole Gerarda Power, Daniella Simas Medeiros

Bibliographic record

VenueCriminal Justice Review · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsMemorial University of Newfoundland
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPrisonOvercrowdingCriminologyWork (physics)Workplace violenceService (business)PsychologyCarry (investment)SociologySuicide preventionPoison controlPolitical scienceEngineeringBusinessMedicineMedical emergencyLaw

Abstract

fetched live from OpenAlex

The potential for violence in prison shapes how correctional officers (COs) carry out their work. Yet, how provincial COs experience violence remains understudied. Using theoretical insights from the literature on workplace violence in caring and service occupations, we analyze observational data and interviews conducted with COs in eastern Canada. We show that COs carry out their everyday work under increasingly strained conditions (e.g., understaffing) and manage prisoners’ (sometimes violent) responses to deteriorating prison conditions (e.g., overcrowding) by engaging in emotional labor. The COs understand workplace violence as an inevitable “part of the job,” which serves to normalize the experience of workplace violence and deflect attention away from the prison conditions which exacerbate and even produce violence.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.899
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.042
GPT teacher head0.344
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 designOther design
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

Citations44
Published2018
Admission routes3
Has abstractyes

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