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Record W3024385512 · doi:10.1522/revueot.v29n1.1130

Les politiques publiques de développement des milieux ruraux : la Politique nationale de la ruralité du Québec revisitée

2020· article· fr· W3024385512 on OpenAlexaffvenueabout
Bruno Jean

Bibliographic record

VenueRevue Organisations & territoires · 2020
Typearticle
Languagefr
FieldSocial Sciences
TopicSocial Sciences and Governance
Canadian institutionsUniversité du Québec à Rimouski
Fundersnot available
KeywordsPolitical scienceHumanitiesPhilosophy

Abstract

fetched live from OpenAlex

Le processus d’élaboration des politiques publiques rurales et l’évolution qu’ont connue ces politiquespubliques sont décrits dans le présent article. Leur émergence, relativement récente, a été rendue possible avec la fin de la croyance selon laquelle les politiques sectorielles, notamment agricoles, pouvaient solutionner les problèmes ruraux. Ensuite, nous présenterons plus en profondeur l’expérience québécoise de soutien au développement des territoires ruraux avec la Politique nationale de la ruralité (PNR) du Québec; nous mettrons l’accent sur les aspects novateurs d’une telle politique pour tirer quelques enseignements de sa mise en oeuvre de 2001 à 2014. À plus d’un titre et comme l’a reconnu l’OCDE, cette politique originale a montré la pertinence de s’intéresser aux facteurs intangibles, par exemple la mobilisation collective, l’engagement citoyen et la gouvernance locale, qui sont souvent des déterminants décisifs de l’évolution socioéconomique des milieux ruraux.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.059
Threshold uncertainty score0.426

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0060.004
Scholarly communication0.0040.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0110.000

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 head0.037
GPT teacher head0.326
Teacher spread0.289 · 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

Citations3
Published2020
Admission routes3
Has abstractyes

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