Gender and Organization Science: Introduction to a Virtual Special Issue
Bibliographic record
Abstract
Gendered processes and outcomes are pervasive in organizational life. They shape how individuals perceive their career prospects, which types of opportunities they pursue, how they get work done within organizations, and how they balance this work with the rest of their life. Organizations themselves also shape and are shaped by gender dynamics, from the ways they design jobs and performance evaluation systems to the assumptions managers make about individuals’ preferences and motivations. This virtual special issue collects together 14 papers published in Organization Science that challenge common understandings about the sources of gender differences in career outcomes, the effects of balancing work–life obligations, and the ways that gender dynamics play out in teams and organizations. An important insight that emerges from a comparison of these studies is that demand effects are often confused for supply effects. What looks like a supply problem—we think that women choose not to aspire to top positions or to jobs in top paying fields—might actually be a demand problem—organizations or jobs look unappealing to women because of past histories of not hiring or promoting women into leadership roles or of making work–life balance appear to be impossible. These studies suggest that essentialist explanations that attribute gendered outcomes to inherent characteristics or choices of women might be too simplistic or inaccurate. Instead, future research would benefit from examining the complex interactions between supply-side and demand-side drivers of gender inequality.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.057 | 0.016 |
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".