Influence of gender equality practices and work–life programs on women in leadership, management, and nonmanagement: The role of industry gender composition
Bibliographic record
Abstract
Gender equality remains a persistent challenge worldwide. Little is known about the impact of equality initiatives in improving women’s representation in organizations in different industry contexts. Drawing on the theory of workplace inequality remediation and signaling theory, we investigate how gender equality practices and work–life programs influence women’s representation in leadership (top management team), lower-to-middle management (LTMM), and nonmanagement, and assess whether industry gender composition moderates these relationships. Using a large archival dataset spanning 7 years, our 1-year lagged panel analyses show that work–life programs are positively associated with women’s representation at all three organizational levels, whereas gender equality practices are not significantly related to women’s representation. Industry-specific results indicate that gender equality practices enhance women’s representation in nonmanagement in female-tilted industries, while work–life programs improve women’s representation in LTMM and nonmanagement in male-tilted/balanced industries. Together, these findings demonstrate that while gender equality practices may be insufficient on their own and work–life programs serve as effective mechanisms for advancing women’s representation, their effects are contingent on industry context. We note theoretical and research contributions and practical implications.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.005 | 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".