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Record W3134028591 · doi:10.31235/osf.io/hbj9k

The Intersection of Race and Gender in Leadership of Cooperatives in North America: Of whom, By whom, and For whom?

2019· article· en· W3134028591 on OpenAlexaff
Ushnish Sengupta

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCooperative Studies and Economics
Canadian institutionsInstitute for Christian StudiesYork UniversityUniversity of Toronto
Fundersnot available
KeywordsRace (biology)Equity (law)IndigenousPopulationIntersection (aeronautics)Political scienceEconomic growthSociologyGender studiesGeographyEconomicsDemography

Abstract

fetched live from OpenAlex

This paper describes the intersection of class, gender and race in the leadership of cooperatives in North America. Movement of labour across North American borders changes the membership of cooperatives as well as the leadership and formation of cooperatives. The socio-economic shifts that affect cooperatives are also accompanied by marginalization of particular populations, including Indigenous communities and racial minorities. International cooperative principles remain ideals to aspire to rather than a reality in practice. Although women and racial minorities have made some advances in equity in cooperatives, racialized women in particular are not represented in leadership positions in cooperatives in proportion to membership in the broader population. On an optimistic note, cooperatives continue to be more egalitarian organizations than other types of organizations and therefore have the potential for leading as positive role models, addressing the intersection of gender and race for other organizations to follow. Women leading cooperatives will form different types of cooperatives than men leading cooperatives in the same industry. Additionally women of colour leading cooperatives will form different types of organizations than traditional cooperatives, providing for enriched plurality of organizational forms required for addressing complex socio-economic problems.

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 score0.132
Threshold uncertainty score0.589

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 studies0.0000.000
Scholarly communication0.0000.000
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 head0.041
GPT teacher head0.234
Teacher spread0.192 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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