Crashing the Boards: A Comparative Analysis of the Boxing Out of Women On Boards in the United States and Canada
Bibliographic record
Abstract
This paper will first provide a critical, comparative look at the Canadian and the federal American responses to the under-representation of women on boards of large, publicly traded corporations. There will be a discussion about the competing conceptions which emerge in addressing the regulation of women on boards in the United States and Canada and why each jurisdiction implemented its policy when it did. The conceptions arising out of questions about under-representation of women on boards tend to fall within two categories: business case rationales and normative rationales. Given the competing conceptions of this issue, this paper will attempt to demonstrate how the regulatory regimes fit within these conceptions and the solutions which follow each conception. An argument will be advanced that not only does each disclosure regime fail to provide a solution to the underlying issue it is attempting to regulate, but also neither regime even advances the goal the regulators purport to be advancing. Finally, a closer look at the polarizing reactions to Bill 826 provides a hint as to the future direction of the American and Canadian debates. This paper will be one of the first to discuss Bill 826 and what it may mean for the U.S. and Canada.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.019 | 0.006 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".