The Morals of the Women on Boards Story: Global Board Gender Diversity Efforts Still Need Fairness-Based Arguments to Move Regulation to the Next Chapter
Bibliographic record
Abstract
The number of women on boards of public companies in the United States and Canada is still staggeringly low despite the fact that both of these jurisdictions have implemented disclosure-based regulation relating to board diversity. Typically, arguments in support of regulation aimed at increasing women's participation on public boards fall into two categories: the business case and the fairness-based (or normative) case. The business case is essentially the idea that women bring some instrumental benefit to the board which leads to improvements in firm functioning or performance overall. While politically attractive, the business case for justifying regulation has yet to convince most of those in the business community in Canada and the U.S. If it had, we would have seen a much greater transformation in board composition in recent years than we have seen. The normative case, which is the idea that women deserve board seats because this is morally right, is less politically attractive because it is often seen to clash with the view, particularly in the U.S., that the role of corporations-and by implication the boards that manage them-is to maximize shareholder wealth.This article uses the Canadian and American experiences to argue:(1) that the normative case, by itself and in combination with the business case, can justify stronger regulation encouraging greater female participation on boards, especially given the role of the board and the involvement of institutional investors; and(2) that securities regulators in each jurisdiction have a critical and appropriate role to play in increasing board diversity.This article provides an overview of various jurisdictions' regulatory regimes aimed at increasing board diversity and the respective increases of female participation on public boards. It goes on to outline the normative case and provides an analysis of the popular critiques and responses thereto. Also included is a description of the business case and its acknowledged weaknesses. Finally, it argues that normative-based rationales for implementing regulation aimed at enhancing gender diversity on public boards justify action by the SEC in the U.S. and provincial securities regulators in Canada, recognizing, however, that it is important for these capital market regulators to stay within the boundaries of their respective mandates and ensure that regulation does not harm the interests of investors or damage capital markets.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.027 | 0.058 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.011 | 0.017 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".