Efficacité des lois contraignantes et des lois souples pour promouvoir la diversité de genre dans les conseils d'administration:une comparaison France/Canada - Legislative versus Regulatory Board Diversity effectiveness:A Comparison between France and Canada
Bibliographic record
Abstract
(VF)Pour favoriser la féminisation des conseils d'administration, certains pays, comme la Norvège ou la France, ont voté une loi contraignante imposant un quota ; d'autres pays comme le Canada ont adopté une loi souple de type comply or explain. L'article propose une comparaison de l'efficacité des lois contraignantes et des lois souples dans le cas de la promotion de la diversité dans les conseils d'administration. L'étude France – Canada de 445 sociétés cotées sur la période 2011-2014 confirme la féminisation plus grande et plus rapide des conseils d'administration en France, sans s'écarter davantage qu'au Canada des normes de recrutement en vigueur.(VA)To improve gender diversity on boards, some countries, such as Norway and France, have passed a hard law with quotas while other countries like Canada use a soft approach. The aim of the article is to compare the effectiveness of soft and hard laws to promote board diversity. The study of 445 listed companies in France and Canada on the period 2011–2014 confirms that board diversity is higher and faster in France than in Canada, without higher deviation from directors’ appointment standards.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".