Decoding the Fabric of Gender Inequality: Psychological Factors and Social Contexts
Bibliographic record
Abstract
This symposium aims to introduce novel psychological states and emphasize the importance of social contexts for gender inequality, suggesting that management scholars must adopt a broader lens beyond the traditional focus on gender disparities in human capital and occupations when considering gender inequality issues in organizations. Specifically, while introducing novel psychological states and individual characteristics for gender inequality, we emphasize the significance of scrutinizing social contexts (i.e., occupation, corporate, and familial environments) where gender inequality is more likely to manifest. By providing specific contexts that are more likely for gender disparities to occur, this symposium intends to not only identify psychological factors that yield different rewards or gendered psychological states contributing to gender inequality but also attempts to examine ‘when’ those gender gaps in psychological factors are more pronounced, providing policymakers and organizational authorities insights into where they need to pay attention to in order to mitigate gender inequality. The Misperception of Gender Economic Equality. Author: Ivuoma Onyeador; Northwestern Kellogg School of Management Author: Sa-kiera Hudson; Haas School of Business, UC Berkeley Author: Natalie Daumeyer; Yale U. Author: Julian Rucker; U. of North Carolina at Chapel Hill Author: Michael W. Kraus; Yale School of Management Author: Jennifer Richeson; Yale U. Extraversion Revisited: How Personalities and Occupations Jointly Shape Gender Pay Inequality Author: Hee Man Park; The Pennsylvania State U. Author: Seunghoo Chung; Hong Kong Polytechnic U. Bridging the Aspiration Gap: The Unexpected Role of Performance Incentives Author: Julia D. Hur; New York U. Tight Ships or Loose Cannons: Couples’ Approaches to Domestic Tasks and Gender Differences at Work Author: Kelly Harrington; Kellogg School of Management, Northwestern U. Author: Alexandra Feldberg; Harvard Business School Author: Kathleen L. McGinn; Harvard U. Mind the Gap: An Integrated Conceptual Review of Research on the Gender Pay Gap Author: Beth Ann Livingston; U. of Iowa Author: Sujin Jeong; U. of Iowa Author: Felice Klein; Boise State U. Author: Anushka Chakroborty; Tippie College of Business, U. of Iowa
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".