Reconceptualizing and Improving Member Participation in Large Cooperatives: Insights from Deliberative Democracy and Deliberative Mini-Publics
Bibliographic record
Abstract
Member control is a central cooperative value that depends on members having sufficient opportunities to participate in decision-making. Most members of large cooperatives participate in decision-making through non-candidacy participation, which entails responsibilities including electing and monitoring their elected representatives and ratifying resolutions and reports. Non-candidacy participation is crucial to ensure that collective decisions and the conduct of representatives are aligned with the interests of the broader membership. However, prior research points to concerns about the level and quality of non-candidacy participation. In this essay, I draw on research on deliberative democracy to propose a novel solution to address these concerns. I begin by disentangling two commonly conflated forms of non-candidacy participation: aggregative and deliberative. I then argue that large cooperatives could improve both forms of participation through the targeted use of deliberative mini-publics. In doing so, I contribute to research on large cooperatives by advancing a novel solution to im-proving non-candidacy participation and cooperative governance more broadly, articulating a more fine-grained conception of participation to inform future research, and identifying a novel way of conceptualizing and enacting expertise in these organizations.
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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 | 0.036 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.038 |
| Scholarly communication | 0.011 | 0.017 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".