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Record W6989157103

Addressing barriers to promotion for female officers in municipal policing in Canada

2020· article· en· W6989157103 on OpenAlexaboutno aff

Bibliographic record

VenueArca (British Columbia Electronic Library Network) · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicPolicing Practices and Perceptions
Canadian institutionsnot available
Fundersnot available
KeywordsPromotion (chess)Variety (cybernetics)Empirical researchSurvey data collectionFace (sociological concept)Ranking (information retrieval)Value (mathematics)
DOInot available

Abstract

fetched live from OpenAlex

Female police officers face numerous barriers to promotion in municipal policing in Canada. For a variety of reasons, including that policing remains male-dominated, there are very few female officers in higher ranking positions in municipal police agencies in Canada. With few female leaders comes a lack of female role models, mentors, and the female influence in decision making at executive levels. This has had a broad effect on how police agencies respond to the communities they serve. This research summarized the value women bring to the policing profession and explored the barriers to promotion identified in the literature that have affected the number of women in senior leadership roles in Canada. The empirical data collected in this study is derived from surveys completed by 413 police officers working in several municipal police organizations in Canada. The intent of the survey was to explore the real and perceived barriers female officers experience in promotion to higher ranks. The results of the survey offered some important findings, including that barriers to promotion for women still exist in Canadian municipal policing. Recommendations are made suggesting ways that police leaders could use the information derived from this research to address real and perceived barriers to promotion for female officers in their respective policing agencies.

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)0.000
metaresearch head score (Gemma)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.593
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0100.000

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 head0.047
GPT teacher head0.294
Teacher spread0.247 · 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 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

Citations0
Published2020
Admission routes1
Has abstractyes

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