Not just good for her: A temporal analysis of the dynamic relationship between representation of women and collective employee turnover
Bibliographic record
Abstract
Many organizations aim to increase the representation of women in their workforce, yet such efforts are often challenged by women’s relatively higher propensity to leave a job compared to men. Overlooked so far has been the temporal relationship between the representation of women and an organization’s collective employee turnover. We suggest that a substantive and rapid increase in the representation of women positively affects women and results in positive spillover effects for men, leading to a decrease in collective turnover. In our theoretical development, we explain how higher representation of women is associated with higher job embeddedness for all employees, which results in a subsequent decrease in collective employee turnover. We use latent curve model (LCM) analysis to examine a population of 499 organizations over a 14-year time span, and find support for our hypotheses. We suggest opportunities for future research and offer implications for practicing managers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".