Advancing Gender Diversity and Inclusion in Organizations
Bibliographic record
Abstract
This symposium features five papers that seeks to advance gender diversity and inclusion in organizations. These studies leverage diverse data sets and methodologies to explore various issues, such as: 1) contemporary stereotypes of men, women, and managers, 2) how vocational interests may explain men’s underrepresentation in female-stereotypic careers, 3) how gender composition affects team decision-making, 4) White women’s leadership advantage in organizations, and 5) how gender and status impact the believability of sexual harassment allegations. Our discussant, Dr. Marla Baskerville Watkins, a renowned scholar in gender and diversity, will close by providing a brief synthesis of the symposium and facilitating an interactive discussion on future research directions. Altogether, this symposium offers new perspectives on gender and leadership diversity and addresses under-examined inequalities by illuminating how social perceptions (e.g., stereotypes, leader prototypes) can shape the career trajectories and experiences of under-represented groups. Advancing Gender Diversity and Inclusion in Organizations Author: Yan Yi Lance Du; University of Illinois at Urbana-Champaign Author: Serena Wee; Author: Julia M Grgic; EBS University of Business and Law Author: Tanja Hentschel; Author: Meir Shemla; EBS University of Business and Law Author: Francesca Manzi; London School of Economics and Political Science Author: Yixiao Shen; Rutgers University Author: Zibo Zhao; The University of Hong Kong Author: Wenxin Xie; Author: Huan You; York University Author: Lei Zhu; York University Author: Karl Aquino; The University of British Columbia Author: Maja Graso; University of Groningen Author: Irina Gioaba; Kean University Author: Celia Chui; HEC Montreal Author: Marla Baskerville Watkins;
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 | 0.017 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.013 | 0.013 |
| Scholarly communication | 0.016 | 0.018 |
| Open science | 0.001 | 0.022 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".