Multi-stakeholder Governance in Cooperative Organizations: Toward a New Framework for Research?
Bibliographic record
Abstract
ABSTRACTDespite the increasing popularity of multi-stakeholder cooperatives, social-economy researchers largely predict that these organizations will fail. Using a “cost of decision-making” approach, these researchers conclude that the governance structure of multi-stakeholder cooperatives makes this organizational model fundamentally untenable. In this paper, we review the empirical evidence available on multi-stakeholder cooperatives, which suggests that different groups of actors are able to govern themselves successfully. Consequently, we argue that the literature that has focused on the management of common pool resources by self-organized groups may be an appropriate body of literature in which to root a research program on these social-economy organizations.RÉSUMÉMalgré la popularité grandissante des coopératives à multiples intervenants, les chercheurs en économie sociale prédisent que ces organisations essuieront un échec. Grâce à une méthode des coûts pour la prise de décisions, ces chercheurs en viennent à la conclusion que la structure de gouvernance des coopératives à multiples intervenants, par sa nature, en fait un modèle organisationnel indéfendable. Dans cet article, nous examinons les éléments de preuve empiriques disponibles sur les coopératives à multiples intervenants, qui suggèrent que différents groups d’actants peuvent réussir à s’autogérer. Par conséquent, nous discutons du fait que la documentation qui porte sur la gestion des ressources communes par les groupes autogérés pourrait constituer un corpus approprié pour établir un programme de recherche sur ces organisations d’économie sociale.
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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 | 0.042 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.005 | 0.046 |
| Scholarly communication | 0.015 | 0.033 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".