New Research Examining Far-Reaching Implications of Masculinity Contest Cultures
Bibliographic record
Abstract
As cutthroat competitions that are played by masculine rules, masculinity contest cultures (MCCs) have powerful effects on individual and organizational behavior, yet research into the potentially far-reaching implications of MCCs is in its infancy. We know little about the implications of MCCs for marginalized identities beyond women, how MCCs manifest in different contexts, such as academia and elite sports, or how an MCC might be perceived and experienced by people of different status within the same organization. Another key concern is how MCCs might be successfully challenged.Through four papers, this presenter symposium addresses such questions, collectively advancing both theoretical and practical developments in the study of MCCs at work. Masculinity Contest Culture Reduces Prosocial Work Behaviors by Promoting Instrumentality Author: Andrea C. Vial; New York University Abu Dhabi Author: Amna Ramadhan; New York University Abu Dhabi Author: Colleen Cowgill; New York University Abu Dhabi Masculinity Contest Culture in High-Performance Sport Author: Katrina Monton; Teachers College of Columbia University Author: Caryn J. Block; Columbia University in the City of New York Academia as a Masculinity Contest Author: Jennifer L. Berdahl; The University of British Columbia Confronting Masculinity Contest Cultures Through Institutional Courage: A Step Toward Racial Equity Author: Angela Workman-Stark; Athabasca University
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.015 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".