Women at the Top: Emerging Research on Women in Top Management Teams (TMTs)
Bibliographic record
Abstract
This presenter symposium consists of four papers focusing on the topic of women’s representation in top management teams (TMTs). The presentations include studies that offer a more nuanced perspective on gendered stereotypes in leadership by relying on diverse theoretical angles, various modern data sources, and different data samples. Top Management Team Diversity: Implications for Product Quality and Recalls Author: Akshat Lakhiwal; University of Georgia Author: Corinne Post; Villanova University Author: George Ball; Kelley School of Business, Indiana University Author: Hillol Bala; Indiana University Bloomington The Effects of Female TMT Representation on Employees’ Career Decisions Author: Tianjiao Xu; Author: Mihwa Seong; King's College London Author: Simon Parker; Author: Ilaria Orlandi; Copenhagen Business School Does Masculinity Pay? How Executives' Masculinity (vs. Femininity) and Gender Affect Compensation Author: Seung-Hwan Jeong; University of Georgia Author: Ilaria Orlandi; Copenhagen Business School Author: Yusen Xia; Georgia State University Leadership Behaviors of Women CEOs: Pronounced Communion and Subtle Agency Author: Ryan Miller; Author: Alison M. Konrad; University of Western Ontario Author: Martha L. Maznevski;
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".