Influence of Women in Leadership on Gender‐Equality, Work–Life, and Domestic Violence Coping Practices: The Role of Chief Executive Officer Gender
Bibliographic record
Abstract
ABSTRACT Achieving gender equality continues to pose a persistent challenge in contemporary workplaces, as indicated by the slow rate of progress and predictions that achieving gender equality may take a century. Responding to calls for a deeper understanding of how women's leadership influences organizational outcomes, we draw on social role theory to examine the relationship between women in senior leadership and gender equality management systems (GEMS), which is a comprehensive “bundle” of gender‐equality, work–life, and domestic violence coping practices, in the context of the gender of chief executive officers (CEOs). Utilizing a large‐scale, longitudinal dataset of 6620 Australian organizations spanning 7 years, our panel analyses reveal that having women in senior leadership positively influences work–life practices and domestic violence coping practices. The results also indicate that women in leadership have a positive impact on gender‐equality practices when organizations are led by a female CEO. By showing how women in senior leadership and a supportive female CEO jointly foster broader adoption of GEMS, we extend and refine social role theory. Our findings also provide robust evidence, drawn from a wide range of organizations, that the leadership of women at multiple levels plays a pivotal role in accelerating progress toward workplace gender equality.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".