Gender promotion gaps across business units in a multiunit organization: Supply‐ and demand‐side drivers
Bibliographic record
Abstract
Abstract Drawing on gender role and gender queuing theories, we employ a multi‐stage process model to investigate demand‐ and supply‐side drivers of gender promotion gaps and to explore variations in these gaps across different business units within an organization. Analyzing 9 years of personnel records from a multiunit European bank, we find that the gender promotion gap is influenced by both supply‐side and demand‐side factors. Specifically, women are less likely than men to express a motivation to change to a new job or move to a different unit within the bank. Those who do express such motivation are as likely as men to be reassigned to new roles, but their moves are less likely to constitute promotions than are men's moves. Furthermore, gender promotion gaps vary significantly within the organization itself. Business units with the most significant gaps are in regions that have fewer available organizational positions to move into, diminishing women's motivation to seek such moves, and have jobs with numerous incumbents, decreasing women's chances to get a new job or secure a promotion upon doing so. This study extends gender role theory by creating a unified theoretical model that incorporates both employee and employer gender role perceptions as drivers of promotions. It contributes to gender queuing theory by demonstrating the theory's relevance to promotion outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".