Women on corporate boards of directors : international research and practice
Bibliographic record
Abstract
Contents:Introduction: Women on Corporate Boards of Directors: International Issues and OpportunitiesRonald J. Burke and Susan VinnicombePART I: INTERNATIONAL PERSPECTIVES1. Women Board Directors in the United States: An Eleven Year RetrospectiveLois Joy2. Women on Corporate Boards of Directors: The Canadian PerspectiveRonald J. Burke and Richard Lebranc3. The Pipeline to the Board Finally Opens: Women's Progress on FTSE 100 Boards in the UKRuth Sealy, Susan Vinnicombe and Val Singh4. Women on Corporate Boards of Directors: The French PerspectiveMairi Maclean and Charles Harvey5. New Zealand Women Directors: Many Aspire but Few SucceedRosanne Hawarden and Ralph Stablein6. 'Glacial at Best': Women's Progress on Corporate Boards in AustraliaAnne Ross-Smith and Jane Bridge7. The Quota Story: Five Years of Change in NorwayMarit Hoel8. Women on Corporate Boards of Directors: The Icelandic PerspectiveThoranna Jonsdottir9. Women on Corporate Boards of Directors in Spanish Listed CompaniesCelia de Anca10. Contrasting Positions of Women Directors in Jordan and TunisiaVal SinghPART II: RESEARCH THEMES11. Championing the Discussion of Tough Issues: How Women Corporate Directors Contribute to Board DeliberationNancy McInerney-Lacombe, Diana Bilimoria and Paul F. Salipante12. Women Directors and the 'Black Box' of Board BehaviourMorten Huse13. Do Women Still Lack the 'Right' Kind of Human Capital for Directorships on the FTSE 100 Corporate Boards?Siri Terjesen, Val Singh and Susan Vinnicombe14. Examining Gendered Experiences Beyond the Glass Ceiling: The Precariousness of the Glass Cliff and the Absence of Rewards Michelle K. Ryan, Clara Kulich, S. Alexander Haslam, Mette D. Hersby and Catherine Atkins15. On the Progress of Corporate Women: Less a Glass Ceiling than a Bottleneck?Dan R. Dalton and Catherine M. Dalton16. ION: Organizational Networking to Harness Local Power for National ImpactSusan M. Adams, Patricia M. Flynn and Toni G. Wolfman17. Women on Corporate Boards of Directors: Best Practice CompaniesHeather Foust-Cummings18. Critical Mass: Does the Number of Women on a Corporate Board Make a Difference?Sumru Erkut, Vicki W. Kramer and Alison M. KonradConclusion: Directions for Future Research on Women on Corporate Boards of DirectorsDiana Bilimoria
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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 | 0.019 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.013 |
| Science and technology studies | 0.008 | 0.015 |
| Scholarly communication | 0.017 | 0.011 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.027 | 0.002 |
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".