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Record W1504824007 · doi:10.4000/pistes.3077

Violence interpersonnelle en milieu de travail : une analyse du phénomène en milieu correctionnel québécois

2006· article· fr· W1504824007 on OpenAlexvenueaboutno aff
Nathalie Jauvin, Michel Vézina, Renée Bourbonnais, Julie Dussault

Bibliographic record

VenuePerspectives interdisciplinaires sur le travail et la santé · 2006
Typearticle
Languagefr
FieldSocial Sciences
TopicWorkplace Violence and Bullying
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPolitical sciencePhilosophySociology

Abstract

fetched live from OpenAlex

Cet article présente des résultats de recherche issus d’une vaste étude auprès des agents de la paix en services correctionnels québécois (ASC). Le principal objectif poursuivi par ce volet qualitatif d’enquête était de comprendre, via des entrevues individuelles et de groupe, les raisons d’un taux particulièrement élevé de violence interpersonnelle entre les membres d’une même organisation de travail. L’analyse des entrevues a permis de conclure qu’une part importante de la compréhension du phénomène de la violence interpersonnelle entre ASC repose sur le recours par ceux-ci à des stratégies défensives développées en réaction aux sources de souffrance auxquelles ils sont exposés, dont, essentiellement : (1) un travail qui combine une double vocation et qui s’accompagne de sentiments de peur et de frustration, (2) des rapports sociaux marqués par un climat de méfiance et (3) une identité fragilisée par le manque de reconnaissance. Des pistes de solution ont également été dégagées de l’analyse.

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)0.003
metaresearch head score (Gemma)0.006
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 score0.137
Threshold uncertainty score0.275

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.003
Science and technology studies0.0230.014
Scholarly communication0.0070.003
Open science0.0020.005
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0050.000

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 head0.006
GPT teacher head0.284
Teacher spread0.278 · 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

Citations4
Published2006
Admission routes2
Has abstractyes

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