More women, more money? The impact of discourse on legal and regulatory initiatives regarding women on corporate boards
Bibliographic record
Abstract
The advancement of women on corporate boards is an oft-discussed social issue in many countries. Little existing scholarship, however, compares the nature of legal and regulatory initiatives across international jurisdictions. Similarly, although there is a plethora of research into the potential economic benefits of increasing the number of women on corporate boards, almost none of the academic literature explicitly considers the nature of the arguments used to support measures to further this goal. This thesis addresses such shortcomings by examining common threads in the arguments around gender diversity on corporate boards and applying doctrinal analysis to characterise the existing (and some proposed) legal and regulatory initiatives that have sought to address the issue. This forms the basis of an exploration of the relationship between the discourses that frame the debate regarding women on corporate boards and the various policy interventions introduced to advance that goal. The thesis uses case studies to trace the relationship between discourse and policy in four countries: Norway, Canada, the United Kingdom and Australia. Analysis of these relationships highlights the complex interplay between discourse and policy implementation and points to three significant conclusions. Firstly, the primary discourses surrounding women on boards are worth attention in their own right. Secondly, discourse affects policy. The assumptions inherent in dominant discourses can pre-emptively exclude certain policy initiatives from consideration, even causing advocates to undermine their own stated aims. Thirdly, and most encouragingly, the resulting analyses likewise suggest that policy initiatives and regulatory measures, once implemented, can impact on discourses and even public attitudes.
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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 | 0.014 | 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 | 0.013 | 0.035 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".