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Record W4389236340 · doi:10.1080/23774657.2023.2288017

“The Top Cop”: Understanding Correctional Officer Recruits’ Motivations Towards Correctional Emergency Response Team Membership

2023· article· en· W4389236340 on OpenAlexafffundabout
Zachary Towns, Rosemary Ricciardelli

Bibliographic record

VenueCorrections · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsMemorial University of Newfoundland
FundersCanadian Institutes of Health Research
KeywordsOfficerPublic relationsService (business)Emergency responsePsychologyPolitical scienceCriminologyLawMedicineBusinessMarketingMedical emergency

Abstract

fetched live from OpenAlex

Correctional officer recruits (CORs) must complete the correctional training program (CTP) mandated by Correctional Service Canada (CSC), in preparation for the plethora of occupational responsibilities they will be tasked with in their occupational role of correctional officer (CO). During training, recruits continue to conceive ideas about their career trajectories. Previous research exploring CO or COR motivations has focused largely on occupational entry and their underlying intrinsic and extrinsic motivations. Little empirical evidence examines the general occupational directions or desires of CORs exiting CTP, particularly their interest in future Correctional Emergency Response Team (CERT) membership. The current study relies on data from interviews with 36 CORs who expressed interest in CERT membership before employment at a federal penitentiary. Findings suggest that these CORs are drawn to CERT membership due to previous occupational interests, media representations of CERT/ERT, but also experience challenges in acquiring membership related to CERT culture, social relationships, and gender. We conclude the article with considerations for recruitment and retention of COs as it relates to CERT membership for Canadian federal correctional services.

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)0.003
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.570
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.006
Science and technology studies0.0110.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.131
GPT teacher head0.371
Teacher spread0.240 · 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; both teacher heads agree on what is shown here.

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

Citations2
Published2023
Admission routes3
Has abstractyes

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