Girls, Who Run The World? Not Yet: An Analysis of the Underrepresentation of Women on Boards in Canada and the Theory Underlying Regulation Thereof
Bibliographic record
Abstract
This thesis provides an in-depth analysis of the underlying theories of the regulation of the underrepresentation of women on boards, typically categorized into business case and normative rationales. An analysis of these theories and their pitfalls reveals that it is time to amend the Canadian diversity policy, which may mean acceptance by the regulators of one, both or neither of these rationales. Through an analysis of the regulatory journey of the Canadian policy contained in NI 58-101F1, it is argued that while regulators claim the policy was rooted in business case rationales, it in fact arose from normative concerns. The policys weakness has not allowed it to achieve its stated objective which is to increase gender diversity on reporting issuer boards. A range of regulatory options more likely to enhance womens participation on boards are presented. What exact form regulation should take is left up to the securities regulators.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.018 | 0.007 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 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".