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Record W2971481026 · doi:10.7202/1062230ar

Le parcours des policières au Québec : des stratégies individuelles à l’approche organisationnelle

2019· article· fr· W2971481026 on OpenAlexvenueaboutno aff
Sophie Brière, Antoine Pellerin, Anne-Marie Laflamme, J. C. K. Laflamme

Bibliographic record

VenueRecherches féministes · 2019
Typearticle
Languagefr
FieldSocial Sciences
TopicEducation, sociology, and vocational training
Canadian institutionsnot available
Fundersnot available
KeywordsPolitical scienceHumanitiesArt

Abstract

fetched live from OpenAlex

Malgré l’ouverture aux femmes dans le milieu policier au Québec, on constate une stagnation de leur présence. Une recherche a été entreprise auprès de services de polices municipaux au Québec pour mieux comprendre le parcours des policières dans leur contexte organisationnel, répertorier les résistances au changement et mettre en évidence des pistes pour favoriser la progression et la rétention des policières. Sur la base des théories féministes et de la théorie institutionnelle ainsi que d’une démarche méthodologique qualitative inspirée de la théorie ancrée et de la connaissance située, les résultats obtenus montrent que la faible progression des policières s’explique par la primauté des changements individuels exigés de leur part en accentuant les différences hommes/femmes et en laissant présager un déficit de compétences. On note également une perte de crédibilité associée à la maternité et la quasi-absence de changements sur le plan organisationnel.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.110
Threshold uncertainty score0.335

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0130.007
Scholarly communication0.0070.002
Open science0.0020.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.001

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 head0.299
GPT teacher head0.428
Teacher spread0.129 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations1
Published2019
Admission routes2
Has abstractyes

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