The Intersection of Race and Gender in Leadership of Cooperatives in North America: Of whom, By whom, and For whom?
Bibliographic record
Abstract
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.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".