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Record W2745287483 · doi:10.4000/pistes.5099

Pénibilité au travail en milieu scolaire, stratégie de faire face et stratégie de défense chez les enseignants débutants : un autre regard sur les éléments contributifs d’une vulnérabilité au phénomène de décrochage professionnel

2017· article· fr· W2745287483 on OpenAlexvenueno aff
Solange Ciavaldini-Cartaut, Hélène Marquié-Dubié, Fabienne d’Arripe-Longueville

Bibliographic record

VenuePerspectives interdisciplinaires sur le travail et la santé · 2017
Typearticle
Languagefr
FieldSocial Sciences
TopicEducation, sociology, and vocational training
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPolitical sciencePhilosophy

Abstract

fetched live from OpenAlex

Cet article porte sur la pénibilité au travail en milieu scolaire dans l’enseignement secondaire en France. À partir de présupposés issus d’une approche transactionnelle du stress et d’une psychodynamique du travail son originalité est d’en documenter la complémentarité pour analyser les facteurs contributifs de cette pénibilité et pour comprendre les stratégies d’ajustement et les mécanismes individuels de défense déployés par les enseignants débutants confrontés aux situations aversives perçues et vécues au cours de leur première année en poste. Les recommandations et les pistes suggérées portent sur la prévention primaire des risques psychosociaux en milieu scolaire et visent à permettre aux enseignants débutants de disposer de ressources à l’issue de leur formation à l’université limitant leur risque de décrochage professionnel.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0090.008
Scholarly communication0.0060.003
Open science0.0010.006
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0130.001

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 head0.079
GPT teacher head0.407
Teacher spread0.328 · 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

Citations8
Published2017
Admission routes1
Has abstractyes

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