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Record W4404127423 · doi:10.1093/police/paae100

Masculinity contest cultures and organizational outcomes in police organizations

2024· article· en· W4404127423 on OpenAlexaffabout
Ryan Buhrig

Bibliographic record

VenuePolicing A Journal of Policy and Practice · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicGender Roles and Identity Studies
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsCONTESTMasculinityOrganizational culturePsychologyCriminologyPolitical scienceSocial psychologySociologyPublic relationsGender studiesLaw

Abstract

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Abstract As a male-dominated occupation that has historically valued strength, risk-taking, and control, policing may be particularly susceptible to masculinity contest cultures (MCC), characterized by social norms that valorize physical ability, avoid weakness, prioritize work, and promote dominance. Through surveys of patrol officers from five Canadian police agencies (n = 238), this study explored the existence of MCCs and their relationship with organizational outcomes. The study suggested that an officer’s perception of their work within an MCC can predict job engagement (b = −0.48, P < .001), work meaning (b = −0.35, P < .001), self-reported performance (b = −0.07, P < .001), and turnover intention (b = 0.07, P < .001) when controlling for gender, supervisory status, years of police service, and university education. Additionally, university degrees were a significant positive predictor of MCC scores (b = 2.40, P < .01), indicating that university-educated police officers perceive their workplace cultures as more masculine. As the first study that related MCCs to university education, job engagement, and work meaning in policing, it advances our understanding and provides insights into how these cultures relate to organizational outcomes. The results also have implications for policy and police administration.

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)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.452
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.029
GPT teacher head0.399
Teacher spread0.370 · 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

Citations10
Published2024
Admission routes2
Has abstractyes

Explore more

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