Women in management worldwide: progress and prospects
Bibliographic record
Abstract
Contents: Women in management worldwide: progress and prospects - an overview, Marilyn J. Davidson and Ronald J. Burke Part I Women in Management - European Union Countries: Women in management in France, Jacqueline Laufer Women in management in Greece, Athena Petraki-Kottis and Zoe Ventoura-Neokosmidi Women in management in the Netherlands, Kea G. Tijdens Women in management in Portugal, Carlos Cabral-Cardoso Women in management in Spain, Mireia las Heras, Nuria Chinchilla and Consuelo LeA^3n Women in management in the UK, Fiona M. Wilson. Part II Women in Management - European Countries: Women in management in Norway, Laura E.M. Traavik and Astrid M. Richardsen Women in management in Russia, Carianne M. Hunt and Sarah E. Crozier. Part III Women in Management - North and Central America: Women in management in Canada, Golchehreh Sohrab, Rekha Karambayya and Ronald J. Burke Women in management in Mexico, Gina Zabludovsky Women in management in the USA, Kimberly Mathe, Susan Michie and Debra L. Nelson. Part IV Women in Management - Australasia: Women in management in Australia, Glenice J. Wood Women in management in New Zealand, Judy McGregor. Part V Women in Management - Asia: Women in management in China, Fang Lee Cooke Women in management in Israel, Ronit Kark and Ronit Waismel-Manor Women in management in Lebanon, Hayfaa Tlaiss and Saleema Kauser Women in management in Turkey, Hayat Kabasakal, Zeynep Aycan, Fahri Karakas and Ceyda Maden. Part VI Women in Management - South America: Women in management in Argentina, Roberto Kertesz and Haydee Kravetz. Part VII Women in Management - Africa: Women in management in South Africa, Babita Mathur-Helm Index.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.034 | 0.007 |
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".