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Record W4414310968 · doi:10.7202/1119554ar

Le travail émotionnel des directions adjointes d’école secondaire : un outil stratégique pour mobiliser les équipes éducatives

2025· article· fr· W4414310968 on OpenAlexaffvenueabout
Karyne Gamelin

Bibliographic record

VenueNouveaux cahiers de la recherche en éducation · 2025
Typearticle
Languagefr
FieldSocial Sciences
TopicEducation, sociology, and vocational training
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsQualitative analysisWork (physics)Data collection

Abstract

fetched live from OpenAlex

Cette recherche qualitative examine le rôle stratégique du travail émotionnel chez les directions adjointes d’école secondaire (DAES) du Québec pour mobiliser leurs équipes. Elle élargit la conception traditionnelle du travail émotionnel, généralement limité à l’affichage d’émotions attendues, en le positionnant comme un outil stratégique de mobilisation en vue de susciter des réponses constructives et atteindre des objectifs organisationnels. Adoptant une approche qualitative, l’étude s’appuie sur trois entretiens réalisés à l’aide de la méthode d’instruction au sosie auprès de trois DAES. Les résultats, sans chercher à les généraliser, révèlent que le travail émotionnel est une pratique quotidienne sur le terrain utilisée pour maintenir des relations harmonieuses, gérer des situations complexes et communiquer des émotions positives de manière intentionnelle. Ce travail émotionnel stratégique permet aux directions adjointes (DA) d’obtenir l’engagement des parties prenantes et de créer un climat favorable au changement et à la collaboration.

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)0.006
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.118
Threshold uncertainty score0.234

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0070.008
Scholarly communication0.0070.003
Open science0.0010.005
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0090.001

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 head0.202
GPT teacher head0.455
Teacher spread0.253 · 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 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

Citations0
Published2025
Admission routes3
Has abstractyes

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