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Record W2150774297 · doi:10.1017/s0008423910000429

Comprendre la mise en œuvre différenciée d'une politique publique : Le cas d'une politique de gouvernance au Québec

2010· article· fr· W2150774297 on OpenAlexaffabout
Francis Garon, Pascale Dufour

Bibliographic record

VenueCanadian Journal of Political Science · 2010
Typearticle
Languagefr
FieldSocial Sciences
TopicSocial Sciences and Governance
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsHumanitiesPolitical scienceSociologyPhilosophy

Abstract

fetched live from OpenAlex

Résumé.En 2001, le gouvernement québécois adopte laPolitique de reconnaissance et de soutien à l'action communautaire. La mise en œuvre de cette politique a mené à des résultats contrastés en fonction des champs d'intervention considérés. Dans cet article, nous soutenons que la mise en œuvre différenciée de la Politique est directement liée à la structuration des acteurs sociaux dans chaque champ d'intervention. Par l'analyse de deux champs, le champ de l'environnement et celui de la défense collective des droits, nous montrons comment, au-delà des explications usuelles des processus de mise en œuvre des politiques publiques qui font intervenir le rôle des acteurs politiques et des réseaux, c'est la prise en compte de l'action autonome des acteurs sociaux qui permet de comprendre la différenciation des trajectoires de mise en œuvre. Abstract.In 2001, the Québec government adopted itsPolitique de reconnaissance et de soutien à l'action communautaire. The implementation of this policy has led to contrasting results depending on the fields of intervention considered. We argue that the different implementation pathways of this policy are directly linked to the structuring of social actors in each field. Using two fields of intervention – the environment and advocacy – as case studies, we show the need to go beyond the usual explanations regarding the implementation of public policies which stress the importance of political actors and policy networks. Without neglecting the role of the latter, the different pathways in the implementation processes of this policy are also largely due to the autonomous action of social actors.

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)0.004
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.088
Threshold uncertainty score0.639

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0090.010
Scholarly communication0.0070.002
Open science0.0010.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0100.000

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 head0.013
GPT teacher head0.289
Teacher spread0.276 · 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

Citations9
Published2010
Admission routes2
Has abstractyes

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