A Link between Gender Equality Initiatives and Women’s Representation in the Industry Context.
Bibliographic record
Abstract
Little is known about the impact of workplace gender equality initiatives in improving women’s representation at different organizational levels and in different industry contexts. We investigate how resource practices, pay equity, and work-life programs influence woman’s representations and whether the gender composition of the industry moderates these relationships. Utilising a large archival panel dataset spanning 7-years from 2013 to 2020, we tested their effectiveness across three levels: top management (TMT), lower to middle management (LTMM), and non-management, including moderating effects of industry gender composition (female-tilted, balanced and male-tilted organizations). Drawing on theory of remediation of workplace inequality, our panel analyses show a positive relationship between resource practices and women in TMT and LTMM, pay equity and women in TMT and non-management, and work-life programs and women’s representation in TMT and LTMM. Drawing on the signaling theory, we further found that work-life programs are most effective in enhancing women’s representation in TMT in male-tilted organizations. The programs seem to be non-threatening to the organizational culture as they show effectiveness across all industries. However, the fact that resource practices in male-tilted industries showed marginally significant and weaker positive relationship with women’s representation in TMT, suggests that there are barriers to those initiatives. Together, these findings suggest that well targeted programs designed to closely link to specific sources of inequality in given industry cultures can effectively deliver 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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.006 | 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".