Is Demography Destiny? Exploring the Influence of Gendered Organizational and Occupational Contexts
Bibliographic record
Abstract
Gender differences in career trajectories and representation in top- tier positions persist today, despite broader progress toward achieving equality in society. A robust body of evidence points to a combination of supply-side (i.e., differences in preferences) and demand-side (i.e., biases and unfair barriers) processes perpetuating gender gaps in career advancement. To develop a comprehensive understanding of these processes, it is crucial to take a multi-level perspective and consider the interplay between men and women and their firms, networks, and occupational contexts. This symposium contributes to this growing area of work by bringing together quantitative and qualitative work that builds and tests theory for how gendered organizational, occupational, and network contexts impact various aspects of men’s and women’s performance, experiences, and choices in the workplace. Protecting the occupation: Incumbent backlash in response to gender diversity in law enforcement Author: Jirs Meuris; U. of Wisconsin-Madison Author: Jennifer M. Merluzzi; George Washington U. Author: Alexis Avery; U. of Wisconsin, Madison Author: Julia Lee Melin; Dartmouth College, Tuck School of Business Exploring positive career implications of feminized behavior for women in male-dominated occupations Author: Tiffany Trzebiatowski; Colorado State U. Author: Teresa Cardador; U. of Illinois at Urbana-Champaign Collaboration-association trade-off: Artist network gender composition and creative product novelty Author: Noah Askin; U. of California, Irvine Author: Sharon Koppman; U. of California, Irvine Author: Michael Mauskapf; Columbia Business School Author: Brian Uzzi; Northwestern U. Investments to responsibilities: Unpacking sponsors' gendered reasons for lending social capital Author: Elizabeth Lauren Campbell; Rady School of Management, U. of California San Diego
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.015 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".