<scp>Trickle‐down</scp> and <scp>bottom‐up</scp> effects of women's representation in the context of industry gender composition: A panel data investigation
Bibliographic record
Abstract
Abstract Little is known about how changing organizational gender composition can enhance women's representation at lower levels (trickle‐down effects) and higher levels (bottom‐up effects), and which contextual elements strengthen or weaken these effects. We built a large panel dataset from archives spanning 2010–2019 to test our theorized trickle‐down and bottom‐up effects across three levels: non‐management, lower through middle management (LTMM), and top management team (TMT), including our theorized moderating effects of industry gender composition (male‐tilted vs. female tilted vs. balanced). Our panel analyses show that bottom‐up effects are strongest in female‐tilted industries, consistent with the gender‐role congruence explanation that women appear to be more fitting to leadership positions when followers are predominantly women. Trickle‐down effects are strongest in male‐tilted industries at the lower levels (LTMM to non‐management), but strongest in female‐tilted industries at the higher levels (TMT to LTMM). Together, these findings suggest that increasing the number of female supervisors and middle managers is effective for bringing more female employees into male‐tilted industries. However, the fact that male‐tilted industries showed no significant trickle‐down effects from TMT to LTMM suggests that senior women in these contexts refrain from acting to support other women's careers in order to avoid highlighting their gender identity.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".