Crashing the Boards: A Comparative Analysis of the Boxing Out of Women on Boards in the United States and Canada
Bibliographic record
Abstract
Women are still severely under-represented on public corporations’ boards of directors in both the US and Canada, despite both jurisdictions implementing diversity disclosure regimes years ago. This paper first discusses and compares the history of corporate governance in the US and Canada leading up to the implementation of each jurisdiction’s diversity policy. Secondly, the debate surrounding the under-representation of women on boards and solutions to this issue are presented. The solutions to under-representation, normally expressed in business case and normative case rationales, and their respective pitfalls are examined. This paper advances the argument that securities regulators in both the US and Canada, while purporting to have implemented the diversity policies to better business and protect investors, were really advancing social justice goals. But because neither policy has had an impact on board gender diversity, neither policy is effective in advancing either business goals or normative goals. Finally, this work will be among the first to analyze California’s recent Senate Bill mandating gender quotas and what this may mean for the US and Canada going forward.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.014 | 0.005 |
| Scholarly communication | 0.006 | 0.002 |
| 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".