Beyond the Glass Ceiling: New Insights on Gender in Leadership
Bibliographic record
Abstract
Despite constituting nearly half of the workforce, women continue to face barriers to workplace advancement and remain underrepresented in leadership positions. This symposium advances the study of gender dynamics in leadership by providing a forum for researchers to showcase novel theoretical and empirical advancements addressing the persistence of gender inequality in leadership. The symposium introduces and expands upon novel and meaningful concepts, such as prescriptive self-stereotypes, leader identity trajectories, and leader vulnerability, and examines their implications for leadership aspirations, workplace relationships, and perceptions of leaders. Across five presentations, the symposium highlights how gender-based leadership barriers (e.g., stereotypes and biases) impact outcomes such as leaders’ career choices, identity development, and relational outcomes. The symposium also identifies mechanisms, such as leader attitudes, behaviors, or perceptions of leaders, which link gender leadership barriers to these outcomes, as well as moderators of these relationships. These findings emphasize the importance of considering contextual factors such as industry norms and individuals’ perceptions and behaviors in understanding and mitigating leadership inequality. This session will provide attendees with actionable insights into how gender dynamics shape leadership processes and outcomes, fostering deeper dialogue and the development of strategies to advance equity in leadership. How Should I Be? Prescriptive Gender Self-Stereotyping and Leadership Aspirations Author: Tanja Hentschel; Author: Michelle Ryan; The Australian National University Reject, Suppress, Recover, or Grow? A Meaning-Making Theory of Women’s Leader Identity Trajectories Author: Parisa Sharif-Esfahani; York University Author: Winny Shen; Vulnerable Leadership: How Leader Gender Impacts Vulnerability's Effect on Trust Author: Elana Zur; Wilfrid Laurier University Author: Lindie Liang; Wilfrid Laurier University Authentic or Unprofessional? Gendered Reactions to Leaders who Disclose a Weakness Author: Ben Keller; University of California Los Angeles Author: Joyce He; University of California Los Angeles Self-Promotion Advice and Unintended Consequences for Women Leaders Author: Pearlyn Ng; University of Waterloo Author: Douglas J. Brown; University of Waterloo Author: Shane Gibson; University of Waterloo
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.020 |
| Scholarly communication | 0.008 | 0.016 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".