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Record W2013959449 · doi:10.3917/es.032.0093

Le discours syndical face à la nouvelle gestion publique dans le système éducatif québécois

2014· article· fr· W2013959449 on OpenAlexaffabout
Christian Maroy, Samuel Vaillancourt

Bibliographic record

VenueEducation et sociétés · 2014
Typearticle
Languagefr
FieldSocial Sciences
TopicSocial Sciences and Governance
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsPolitical scienceHumanitiesPhilosophy

Abstract

fetched live from OpenAlex

Cet article porte sur la nature du discours des syndicats d’enseignants face à la nouvelle gestion publique (NGP) dans le système éducatif québécois. Il situe le discours syndical de manière transversale en analysant comment il intègre et façonne différents lieux communs (catégories cognitives, théories causales, topiques privilégiées) partagés par la plupart des parties prenantes s’exprimant sur la NGP en éducation dans les médias ou la scène politique institutionnelle. Il propose une analyse de contenu des discours de ces acteurs syndicaux en tant qu’action dans le processus de concertation politique lié à la NGP en éducation et étudie leurs interventions en commission parlementaire. L’article montre que leur action dans ce forum a, au-delà des oppositions visibles, conforté la trajectoire néo-statiste imprimée à la NGP de l’éducation au Québec. L’analyse du discours révèle que les acteurs syndicaux se sont largement distanciés des acteurs gouvernementaux et des principales autorités éducatives face à la gestion axée sur les résultats. De façon paradoxale, le discours syndical et la dialectique des récits d’action publique lors des audiences parlementaires ont contribué à renforcer la voie néo-statiste du nouveau management public québécois, contre une voie plus néolibérale.

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)0.008
metaresearch head score (Gemma)0.024
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.966
Threshold uncertainty score0.536

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.024
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0150.011
Scholarly communication0.0070.003
Open science0.0010.005
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0170.001

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 head0.031
GPT teacher head0.387
Teacher spread0.356 · 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 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

Citations6
Published2014
Admission routes2
Has abstractyes

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