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Record W4411747057 · doi:10.1002/bse.70045

How Cooperatives Embed Circularity in Their Business Models and Governance—Results From an International Survey

2025· article· en· W4411747057 on OpenAlexafffund
Rafael Ziegler, Jonas Rey‐Sierro, Sonja Novković, Inmaculada Buendía‐Martínez, Justine Ballon, Simon Teasdale, Michael J. Roy

Bibliographic record

VenueBusiness Strategy and the Environment · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicSustainable Supply Chain Management
Canadian institutionsSaint Mary's UniversityDesjardinsHEC Montréal
FundersSocial Sciences and Humanities Research Council of CanadaGovernment of Canada
KeywordsCorporate governanceBusinessProcess managementInternational businessIndustrial organizationKnowledge managementEconomicsManagementComputer scienceFinance

Abstract

fetched live from OpenAlex

ABSTRACT Cooperatives as a democratic form of economic organization are an emerging focus in research on the social dimensions of circular economy. However, there is no international database on cooperatives and circular economy, impeding systematic analysis. Drawing on the first international database and survey of cooperatives and circular economy from 12 countries in Europe and the Americas, we explore how cooperatives embed circularity in their business models and governance, including the technology and partnership choices this involves, and how this uptake is currently facilitated. The analysis shows there to be a trend of new, usually small cooperatives that pursue circularity as a core value from inception, that tend to prefer upstream circularity strategies and that look for support from established cooperative networks. Already established, also larger cooperatives tend to explore circularity strategies both internally and by forming networks. There is potential for sectoral and intersectoral federations to facilitate circularity uptake.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.766
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.002
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 head0.019
GPT teacher head0.213
Teacher spread0.194 · 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

Citations6
Published2025
Admission routes2
Has abstractyes

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