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Record W4389787376 · doi:10.37725/mgmt.2023.8478

Reconceptualizing and Improving Member Participation in Large Cooperatives: Insights from Deliberative Democracy and Deliberative Mini-Publics

2023· article· en· W4389787376 on OpenAlexaff
Simon Pek

Bibliographic record

VenueM n gement · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCooperative Studies and Economics
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsCandidacyDeliberative democracyPublicsDemocracyPublic relationsPolitical scienceValue (mathematics)Corporate governanceControl (management)Public administrationSociologyPoliticsLawManagementEconomicsComputer science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.320
Threshold uncertainty score0.872

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.036
GPT teacher head0.269
Teacher spread0.233 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2023
Admission routes1
Has abstractyes

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