La construction du champ de l’économie sociale une analyse des tensions autour des enjeux juridiques et de capitalisation
Bibliographic record
Abstract
Le projet de faire evoluer le droit associatif pour repondre aux besoins de capitalisation des associations d’economie sociale (AES) participe a la construction du champ de l’economie sociale et implique non seulement les AES et l’Etat mais egalement d’autres associations, en particulier celles de l’action communautaire autonome, et les cooperatives. Car un champ institutionnel peut etre vu comme une aire sociale de jeu commune construite par des acteurs et des actants (comme les lois existant deja) en relation autour d’enjeux particuliers (cadre juridique, capitalisation) mais aussi fondamental comme un modele alternatif de developpement. Comme tout phenomene social, la construction - jamais achevee - d’un champ institutionnel produit des tensions : la tension entre la stabilite et le changement, la tension entre la separation et l’integration et la tension entre l’ouverture et la fermeture. Dans le cadre de la consultation gouvernementale sur le projet de reforme du droit des associations personnalisees (Partie III de la Loi sur les compagnies, Quebec), une premiere analyse de ces tensions a ete realisee (Malo et Berard, 2009). Apres la presentation des deux modeles theoriques mobilises (Camus 2006 et Audebrand et Malo, 2010), l’analyse des tensions est developpee ainsi que les reponses aux tensions.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.007 | 0.016 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".