Gender Norms in the Workplace are Exclusionary (to Some) and Restrictive (to All)
Bibliographic record
Abstract
Work culture often mirrors societal gender norms, promoting employee behaviors and traits traditionally associated with specific gender roles. Some workplaces emphasize male-stereotypic attributes (e.g., competitiveness), some promote female-stereotypic qualities (e.g., communality), and some foster relatively gender-neutral norms. The five talks in this symposium shed light on how gendered work cultures shape workers’ self-views and how they are viewed by others, constraining their behavior and undermining diversity. The first three talks focus on masculine cultures, highlighting their negative consequences not only for women but also men who deviate from traditional gender roles or who belong to racial or sexual minorities. The first talk (Ward) explores the backlash faced by male employees who violate traditional gender norms in male-typed workplaces. The second talk (Plückelmann) discusses how emphasizing masculine norms discourages women from applying for leadership positions. The third talk (Spielmann) explores how masculinity workplace norms shape status perceptions across gender, race, and sexual orientation, and how this translates into reduced organizational attraction among socially disadvantaged groups. The last two talks focus on the impact of gender-inclusive and feminine workplace cultures. The fourth talk (Nater) discusses how inclusive (vs. competitive) norms positively impact leadership behaviors and career aspirations for both men and women. The fifth talk (Beneda) outlines the characteristics of underexplored feminine workplace cultures, introduces an instrument to measure them, and discusses effects on relevant psychological outcomes. Collectively, the five talks highlight how gendered workplace cultures might negatively impact all employees by restricting their behavior and how they foster gender segregation. Examining Backlash Against Fathers Taking Parental Leave in Male-Dominated Workplaces Author: Joseph Ward; Dublin City University Author: Janine Bosak; Dublin City University Do Women (More Than Men) Hesitate to Apply for Leader Roles Unless They Meet All Job Requirements? Author: Clara Plückelmann; Stockholm University Author: Christa Nater; University of Bern Author: Sabine Sczesny; University of Bern Masculinity Contest Culture and the Status Divide:How Work Norms Shape Marginalized Groups’ Interest Author: Julia Spielmann; New York University Abu Dhabi Author: Andrea C. Vial; New York University Abu Dhabi How Gender-Inclusive Workplace Norms Free Women—and Men—From Masculine Defaults Author: Christa Nater; University of Bern Author: Jacklyn Koyama; University of Toronto Author: William Hall; The University of British Columbia Author: Emily Cyr; York University Author: Toni Schmader; The University of British Columbia Toward a Theory of Femininity Workplace Culture: Conceptualization and Scale Development Author: Marta Beneda; New York University Abu Dhabi Author: Andrea C. Vial; New York University Abu Dhabi
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.017 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".