[Under]Representation of Women in Leadership: Where Does the Onus Lie?
Bibliographic record
Abstract
The underrepresentation of women in senior leadership roles continues to generate interest among lay people, policy makers, and academics. At least two distinct lines of research have emerged trying to understand this phenomenon, some academics have dedicated their efforts to understand factors on the supply side, investigating mechanisms used to upskill women and get them ready for leadership roles (e.g., training, mentoring, networking). Almost in parallel, other academics have been interested on the demand side, that is, strategies that aim at increasing the demand for qualified women (e.g., reporting requirements, targets and quotas for women in leadership). In the current symposium, we try to integrate these two lines of research by presenting four studies that are focused on supply-side strategies, demand-side strategies, and both. With a stronger focus on the supply side, we will have two papers within the university context, where the inequalities in access to quality education, networks, and mentors is likely to originate. On the demand side, the papers in this symposium will analyse the effectiveness of hiring and employment practices in the context of boards of directors of US top firms. The symposium will finish with a facilitated discussion about the limitations in the theorising within the supply-side and the demand-side research, potential integrative frameworks, and the policies and practices that would be reasonable to implement based on the research findings. Is It All About Who You Know? Presenter: Alicia R. Ingersoll; Utah State U. Presenter: Alison Cook; Utah State U. Presenter: Christy Glass; utah state A Matter of Choice? Gender Differences in Professors Participation in Academic Deanship Presenter: Levke Henningsen; U. of Zurich Presenter: Alice H Eagly; Northwestern U. Presenter: Klaus Jonas; U. of Zurich Criteria Versus Process: Selection Practices and Female Representation on Boards of Directors Presenter: Gosia Mikolajczak; La Trobe U. Presenter: Robert E. Wood; U. of Melbourne Presenter: Melissa Wheeler; Faculty of Business and Economics, U. of Melbourne Presenter: Victor Sojo Monzon; Centre for Workplace Leadership, The U. of Melbourne Gender Diversity in Corporate Board Committees Presenter: Arjun Mitra; U. of Illinois at Chicago Presenter: Steve Sauerwald; U. of Illinois at Chicago
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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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".