Do We Need a New Direction for Managing a Multi-Stakeholder Co-operative?A Critical and Theoretical Reflection on Why Aspirations Sometimes Fail
Bibliographic record
Abstract
Central to this paper is a case study examining stakeholder management in a multi-stakeholder co-operative firm from Ontario, Canada focused on the role of the Democratic Member Control principle and operational interactions in finding common ground among stakeholders. The actor's aspiration in the case study is for economic democracy and social inclusion and the development of a local sustainable economy providing food security for all. The authors' first claim is that this case study, whilst a modest one in itself, is representative of many small bottom-up initiatives taken by people anxious to make a difference in their communities informed by co-operative values and principles. Although there have been notable success stories such as the ones in Mondragon Spain and Kobe Co-op in Japan, even these examples have met with challenges and questions of long-term viability in the context of globalisation
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".