Gender Diversity in the Boardroom: Raising Questions About the "Comply or Explain" Model and Targets in Canada
Bibliographic record
Abstract
Effective corporate governance requires diversity in perspectives. Nevertheless, gender disparity continues to be a long-standing and prevalent problem on Canadian boards of directors and in executive roles. A “business case” argument that asserts that diverse leadership achieves better financial results has been put forward in support of rectifying gender disparity; however, recent meta-analyses research denies the validity of the “business case” argument. This paper argues that conclusions regarding the invalidity of the business case should be approached with caution. In 2014, securities regulators in Canada implemented amendments to Form 58-101F1 Corporate Governance Disclosure in order to address gender diversity. Unfortunately, progress has been slow because the new diversity disclosure rules are not based on a true “comply or explain” model. This paper argues that securities regulators should require publicly traded companies to adopt a policy relating to the representation of women on their boards. Furthermore, this policy should include a target percentage, chosen by the company, for women on a company’s board of directors and in their executive officer positions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.030 | 0.085 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.019 | 0.013 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".