Novel Insights on Improving Gender Balance
Bibliographic record
Abstract
Women’s representation in powerful, high-income positions such as in Top Leadership and in Science, Technology, Engineering and Mathematics (STEM) still dramatically lags behind men’s. Ameliorating this underrepresentation and imbalance is important in order to increase the global talent pool and improve sustainability (UN Sustainability Goal #5: to achieve gender equality and empower all women and girls). In this symposium, four empirical talks will summarize recent research and developing insights on solutions to gender inequality. Following these talks, a preeminent scholar of psychology and of management and organizations will serve as discussant to integrate these new insights on solutions into established ways of thinking about women’s professional challenges. Although the causes of gender inequality in leadership and STEM fields (e.g., gender stereotypes and prejudice, masculine work environments) have been understood for some time, far less is known about how to mitigate these deleterious effects. Addressing this relative gap in the literature, this symposium introduces (1) something women can do to empower themselves, (2) something organizations can do to increase women’s interest in leadership positions, (3) something men can do to help women succeed, and (4) how both women and men are motivated to be change agents for achieving gender balance. Social Network Centrality Empowers Women to Confront Sexism Presenter: Aneeta Rattan; London Business School Presenter: Raina A. Brands; London Business School Managing Gender Balance: How Policy Interventions May Increase Womens Striving for Leadership Presenter: Christa Nater; U. of Bern Presenter: Sabine Sczesny; U. of Bern Interpersonal and Institutional Signals of Identity Threat in the Workplace Presenter: William Hall; U. of Toronto Presenter: Toni Schmader; U. of British Columbia Presenter: Audrey Aday; U. of British Columbia Presenter: Michelle Inness; U. of Alberta Presenter: Elizabeth Croft; U. of British Columbia Predicting Gender Balance Motivations and Actions among Global Leaders Presenter: Clarissa Cortland; INSEAD Presenter: Zoe Kinias; INSEAD
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.025 | 0.002 |
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".