It's Complex: Conditions That Inhibit Women's Inclusion at Work
Bibliographic record
Abstract
Women’s inclusion in the workplace has continued to improve since the gender “revolution” of the 1960s, yet women are far from reaching parity with men and improvements have not been equal for all groups of women. Contemporary gender discrimination is complex and often manifests as conditional bias such that gender interacts with other phenomena (e.g., attributes, conditions, etc.) that result in unequal effects for men and women, or for women of different racial or ethnic groups. The five papers in this symposium highlight the complexity inherent in women’s experiences at work by examining how different work conditions interact to affect women’s inclusion and equity in the workplace. Each paper highlights a distinct and important combination of simultaneously occurring factors or conditions that do not have the same effect when examined alone. Such combinations range from the intersectionality of gender with race or religion, to the specific co-occurrence of behavior (i.e., crying and apologizing) and context (i.e., after making a mistake) that affect men and women differently. This symposium addresses the subtle nuances of gendered experiences faced by today’s workers, which may help managers and organizations create more inclusive work environments for men and women. Boys Don’t Cry Crocodile Tears: The Asymmetric Effects of Crying on Desire to Punish Men and Women Presenter: Natalya Alonso; U. of British Columbia Presenter: Nathan Dhaliwal; U. of British Columbia Presenter: Ela Bandari; U. of British Columbia Presenter: Jennifer L. Berdahl; U. of British Columbia How Women Rationalize Themselves Out of Leadership Roles: Unintended Consequences of Job Crafting Presenter: Jasmien Khattab; U. of Virginia Darden School of Business Presenter: Morela Hernandez; U. of Virginia Darden School of Business Missed, Dissed, or Dismissed? Why Incivility towards Women Goes (Un)noticed Presenter: Jamie Lee Gloor; U. of Zurich Presenter: Tyler Gene Okimoto; U. of Queensland Presenter: Xinxin Li; Antai College of Economics and Management, Shanghai Jiao Tong U. More Than Just a Headscarf: How Organizations May Be Excluding Muslim Women Presenter: Shannon Cheng; Rice U. Diversity Policies Supporting Racial Minority Women: Not So Supported Presenter: Ivona Hideg; Wilfrid Laurier U. Presenter: Lance Ferris; Michigan State U. Presenter: Christy Zhou Koval; Eli Broad School of Business, Michigan State U.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".