Increasing the Number of Women on Corporate Boards: Comparing The “Comply or Explain” and Quota Approaches, Which Is the Most Practical and Effective for Canada?
Bibliographic record
Abstract
This thesis investigates the practicality and effectiveness of comply or explain and quota approaches to improving the participation of women on corporate boards, drawing on the experience in Canada (focusing on Ontario) and Europe (focusing on Norway). Relevant institutional and contextual factors that have a bearing on gender diversity are explored, using the ecological model and sustainable governance framework. This research utilizes semistructured interviews with key participants from government, the private sector and civil society. The thesis finds that due to particular characteristics of the Canadian comply or explain law (with disclosure being the starting point for involvement of non-state actors in implementation), as well as distinctive characteristics of the Canadian institutional context (where increased participation has been achieved through a combination of state and non-state action without use of quotas), the comply or explain approach appears to be the most practical and effective in Canada’s distinctive institutional context at this time. The thesis also recommends changes to improve the effectiveness of the current Canadian comply or explain approach.
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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 | 0.017 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.014 | 0.007 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".