Addressing Gender Inequality in Organizations: New Insights on the Impact of Employer Practices
Bibliographic record
Abstract
Employer practices powerfully shape gender inequality in organizations by allocating opportunities and specifying how work should be accomplished. In this symposium, we bring together scholars who share a concern about understanding how employer practices can be a source of change for reducing gender inequality in organizations. The five papers in this symposium each shed light on how particular employer practices—remote training, sorting of employees into career paths, assignment of work tasks, tenure extensions, and work-life policies—affect women’s wage and career outcomes. Our discussant, Lauren Rivera, a leading scholar in the study of workplace inequality, will close our symposium by synthesizing the presented papers and facilitating a discussion with the audience regarding the future directions for this important topic. Through this symposium, we aim to generate new insights about how scholars can continue to study and improve the research on employer practices and gender inequality in organizations.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.018 |
| Scholarly communication | 0.012 | 0.019 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| 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".