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Record W3111708664 · doi:10.1093/police/paaa061

Who Am I? Who Are We? Exploring the Factors That Contribute to Work-Related Identities in Policing

2020· article· en· W3111708664 on OpenAlexaffabout
Angela Workman-Stark

Bibliographic record

VenuePolicing A Journal of Policy and Practice · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicPolicing Practices and Perceptions
Canadian institutionsAthabasca University
Fundersnot available
KeywordsCONTESTMasculinityIdentification (biology)Social identity theoryPsychologyPerceptionSocial psychologyIdentity (music)Work (physics)Political scienceSocial groupEngineering

Abstract

fetched live from OpenAlex

Abstract Using social identity theory, this study examines the conditions under which police officers become attached (or not) to their organization and to their work, and whether one’s sex influences these relationships. Through an analysis of secondary survey data collected from a large Canadian police organization, the study found that fair treatment and psychological safety were significantly related to officers’ identification with their organization, and in turn, their work. The findings also demonstrated that when officers perceived their workplace as a masculinity contest, they were less likely to identify with their organization. Additionally, perceptions of a masculinity contest were associated with a greater likelihood that officers reported lower levels of psychological safety, and this effect was more significant for female officers. While women overall were no less likely than men to be attached to their organization or their occupational role, women who perceived their workplace as psychologically less safe reported lower levels of identification. The study also found that race and level within the organization may have a greater effect than sex on work-related identification. Overall, the study makes a significant contribution to the nascent literature on work-related identification and policing, as well as to the body of research on women in policing.

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)0.013
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 score0.552
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.003
Open science0.0000.000
Research integrity0.0000.001
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 head0.204
GPT teacher head0.412
Teacher spread0.208 · 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

Citations6
Published2020
Admission routes2
Has abstractyes

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