Les facteurs de succès dans le développement du loisir en milieu rural
Bibliographic record
Abstract
A l’evidence, le milieu rural est moins bien pourvu que le milieu urbain autant en institutions qu’en capacite des municipalites de jouer le meme role. Quels modeles prevalent, et surtout, quels modeles peuvent assurer une perennite aux services a la population en milieu rural? Au Quebec, au plan local, le loisir, le sport, la vie communautaire et culturelle sont l’affaire du partenariat entre la municipalite et la societe civile, partenariat soutenu par quelques institutions publiques comme les ecoles, les etablissements et les organismes du reseau de la sante et des services sociaux. Dans ce systeme, les municipalites jouent un role de pivot et de pilote d’un reseau plus ou moins formel. Au cours des derniers mois, le Laboratoire en loisir et vie communautaire, en collaboration avec differents partenaires, a entrepris une demarche de recherche sur les facteurs de succes et d’echec du loisir en milieu rural. Ce bulletin de l’Observatoire quebecois du loisir presente le contexte, la methodologie et les resultats preliminaires de cette recherche, resultats valides recemment par 125 maires et conseillers municipaux reunis a l’occasion du congres de la Federation quebecoise des municipalites.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".