“Bringing Canadian Women on Board”: A Behavioural Economics Perspective on Whether Public Reporting of Gender Diversity Will Alter the Male-Dominated Composition of Canadian Public Company Boards and Senior Management
Bibliographic record
Abstract
In the majority of jurisdictions with developed public markets, the debate on the gender diversity of boards and senior management, practically speaking, has moved from “why” to “how” in recent years. It is now arguably far more accepted that increasing the percentage of women on corporate boards is good for society and good for business. The new debate surrounds the mechanisms needed to increase the number of qualified women on boards and in senior management positions. Several jurisdictions around the world have tackled the issue by implementing fixed quotas. Other jurisdictions have followed a softer touch approach by requiring listed companies to comply with gender diversity targets or explain their failure to do so. Largely supported by industry, the Ontario Securities Commission adopted a disclosure model in 2014, which was subsequently followed by eight other Canadian jurisdictions. This model requires TSX-listed and other non-venture issuers to comply with disclosure requirements for a range of gender diversity initiatives in their annual public corporate governance disclosure or explain why they do not comply. This article considers the gender bias and implicit prejudice that the new disclosure regime attempts to remedy and the effectiveness of such a disclosure regime in the context of behavioural economics heuristics and theories, focusing specifically on the “debiasing through law” versus “debiasing law” methodology presented by Christina Jolls and Cass R. Sunstein. Based on this analysis, suggestions are made to increase the effectiveness of the new disclosure regime, including whether the quota debate should be re-opened if meaningful quantitative evidence of increased gender diversity is not demonstrated by 2018.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.012 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".