Worker Empowerment through Multi-Stakeholder Governance? A Solidarity Co-operative Case Study
Bibliographic record
Abstract
Multi-stakeholder co-operatives (MSCs), which allow for multiple parties (both consumer and worker, for example) to share in governance, have come to define a number of regional co-operative models worldwide and are often characterized as particularly inclusive and capable of expanding the democratic capacity of the co-operative movement. In Quebec, the solidarity cooperative—a multi-stakeholder model through which ownership is divided between multiple parties including workers, consumers, and “supporting members” who often represent other organizations —has proliferated significantly in the last several decades and directly encourages the formation of networks across the social economy through its unique governance structure. In addition to the strengths of this model, a number of challenges and tensions arise from its hybridization of worker and consumer co-operative models. This master's thesis examines these tensions from a worker’s perspective using a participant observation case study of The Hive Café, a solidarity cooperative operating out of Concordia University in downtown Montreal. Both formal and informal divisions between workers and other groups within solidarity cooperative governance are explored with the aim of extracting insights useful to those seeking to build socially-oriented economic alternatives.
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.010 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| 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".