Women on Corporate Boards: A Comparison of Parliamentary Discourse in the United Kingdom and France
Bibliographic record
Abstract
In 2013 the European Commission presented a draft directive calling for member states to increase the presence of women on corporate boards. Some countries, such as France, have taken a quota approach by passing legislation requiring corporations to increase the numbers of women on their boards over time, while the governments of other states, such as the United Kingdom, have preferred measures to encourage corporations to have more inclusive boards. While there is a growing literature on the impact that an increased presence of women can have on corporate boards, as well as a solid feminist literature on the role of quotas in political structures, there has been relatively little attention to the specific ways in which political actors have viewed the question of women on corporate boards. This article compares the ways in which quotas for women in corporate boards have been examined by the legislatures of the United Kingdom and France, with attention also to parliamentary debates in Canada and Russia. It is hypothesized that variations in political discourse help explain why conservative governments adopted such different approaches toward gender balance on corporate boards.
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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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.015 | 0.011 |
| Scholarly communication | 0.014 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".