Co-operative democracy : board diversity and the representation of women
Bibliographic record
Abstract
The principle of democratic governance is a defining feature of co-operative organizations.Members of the board of directors are the elected representatives of the membership, and as democratic organizations, there is an expectation that co-operatives should have adequate representation of member groups on their boards.However, empirical evidence indicates that many Canadian co-operative boards embody a "diversity problem": there is little diversity in board representational characteristics.The main objective of this study is to identify key barriers and facilitators of representational diversity in co-operative leadership structures.To this end, two core research questions are posed.First, how do boards achieve diversity?Second, how do diverse boards maintain their diversity?Board diversity is defined as at least 30 per cent female representation on the board.Factors in four key areas believed to affect board diversity levels are identified: problem recognition; formal diversity policies; proactive recruitment strategies; and responsive governance.For each area, two specific theoretical propositions are posited and examined.Two credit union boards of directors are selected as case studies.The first case is the Surrey Metro Savings Board of Directors between 1995 and 2002, which was homogenous in terms of its demographic composition.The second case is the Coast Capital Savings Board of Directors between 2001 and 2006, which was diverse in terms of its demographic composition.In view of the theoretical propositions that fall under the four main areas of inquiry, each case is examined separately, after which a crosscase analysis is conducted.ii The case study findings support the view that, to achieve diversity, boards must recognize representational homogeneity as problematic, make diversity a priority issue, and take deliberate action towards increasing their diversity levels.These findings also support the view that, to maintain their diversity, diverse boards must have an inclusive governance approach and provide all board members with meaningful opportunities to participate in decision making processes.This study contributes to a greater understanding of how co-operative organizations can rectify the under representation of key groups within their own organizations and communities, and empower those who typically sit on the margins of economic, social and political power.iii * Denotes non-incumbents that were elected between 2001 and 2006 ** Denotes those who were elected in 2002 but stepped down after the 2002 merger with SMS
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".