Economie collaborative et cooperative: freins et leviers des cooperatives de plateforme au Québec
Bibliographic record
Abstract
L'economie collaborative est en pleine croissance et le mouvement devrait s'accelerer dans les prochaines annees. A l'heure actuelle au Quebec, selon l'Observatoire sur la consommation responsable (OCR), ce marche equivaudrait a 2,4 milliards $CA. Toutefois, l'economie collaborative souleve une serie d'enjeux importants en ce qui concerne notamment les conditions de travail, la fiscalite et le modele de gouvernance des entreprises offrant de tels produits et services. Base sur ses valeurs sociales et communautaires fortes, le mouvement cooperatif pourrait profiter de cette occasion pour se positionner et se renouveler. L'economie collaborative offre au mouvement cooperatif une opportunite en ce sens, qui pourrait du meme coup contribuer a compenser certains problemes decoulant de l'etat actuel du marche de l'economie collaborative. Afin d'etablir la strategie a adopter, le Conseil quebecois de la cooperation et de la mutualite (CQCM), en collaboration avec la Maison de la cooperation du Montreal metropolitain (MC2M), a mandate le Groupe de recherche en gestion et mondialisation de la technologie (GMT) de Polytechnique Montreal pour effectuer une etude sur les cooperatives et l'economie collaborative. Le present rapport fait une recension de la litterature sur ce theme. Il detaille ensuite quatre etudes de cas de cooperatives offrant des produits et services sur une plateforme numerique. Il se conclut par une serie de constats et de recommandations.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 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".