Girls, Who Run The World? Not Yet: An Analysis of the Underrepresentation of Women on Boards in Canada and the Underlying Theory of the 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. In particular, it focuses on the Canadian board gender diversity policy found in National Instrument 58-101F1. The theories justifying regulation of this issue are typically categorized into business case rationales and normative rationales. Through an analysis of the regulatory journey of the Canadian policy, it is argued that while securities regulators claim that the policy contained in NI 58-101F1 was rooted in business case rationales, it in fact arose from normative concerns. Not only that, but because of the policy’s weakness, it does not achieve its stated objective which is to increase female participation on reporting issuer boards. It is further argued that neither the business case nor the normative case have been accepted by those on Canadian public boards. A deep analysis of these theories, their pitfalls, and the possibility to combine the two, reveals that it may be time to amend the Canadian diversity policy even if this means the acceptance by the regulator and the business community of one, both or neither of the business or normative cases. The final chapter presents a range of regulatory options which are more likely to enhance women’s participation on public boards than the current policy. Which of these options and what exact form regulation should take are questions this thesis leaves 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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.021 | 0.009 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".