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Record W4223435436 · doi:10.1177/17488958221087488

What it takes to be a “Good” correctional officer: Occupational fitness and co-worker expectations from the perspective of correctional officer recruits in Canada

2022· article· en· W4223435436 on OpenAlexafffundabout
Marcella Siqueira Cassiano, Brittany Bennett, Elizabeth Andres, Rosemary Ricciardelli

Bibliographic record

VenueCriminology & Criminal Justice · 2022
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsMemorial University of Newfoundland
FundersCanadian Institutes of Health ResearchMemorial University of Newfoundland
KeywordsOfficerPerspective (graphical)PsychologyCriminologySocial psychologyApplied psychologyPolitical scienceLawComputer science

Abstract

fetched live from OpenAlex

Selecting individuals who are the right "fit" for correctional work is not an easy task for prison administrators because of the dangerous nature of correctional work and the centrality of prison employees in the prisoner's rehabilitation process. We analyze fitness for correctional work from the employee's perspective, complementing the scholarship focused on the employer's view. We measure occupational fitness in terms of co-worker expectations, analyzing 104 semi-structured interviews conducted with Federal Canadian Correctional Officer recruits in 2018/2019. Recruits in our sample expected a correctional officer to be accountable, reliable, and confident. Understanding the mind-set of new hires provides insights into the correctional officer role and allows employers to align employer-employee expectations, as well as review training and recruitment, which can improve the employee well-being and reduce turnover rates.

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)0.002
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 score0.222
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 integrity0.0000.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.205
GPT teacher head0.455
Teacher spread0.249 · 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

Citations11
Published2022
Admission routes3
Has abstractyes

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