Multiple Identities and Non-Prototypicality: Intersectionality at Work
Bibliographic record
Abstract
Everyone has multiple social identities that influence the way they are perceived and treated and, in turn, how they perceive and respond to their social worlds at work. To date, research has tended to focus only on one identity at a time. The papers in this symposium investigate how different combinations of multiple identities affect a variety of outcomes at work, from biases and beliefs to recruitment, fundraising, and perceptions of speech and discrimination. This symposium has three goals: (1) to shed light on emerging theory on intersecting social identities, (2) to present innovative scholarship about how multiple intersecting social identities affect how individuals negotiate their workplaces and what treatment they receive, and (3) to create space for complicating the conversation about diversity and inclusion in order to examine the complexities of intersectionality and non-prototypicality. Black Women’s Experiences of Mistreatment and Withdrawal Presenter: Kathrina Robotham; U. of Michigan Presenter: Veronica C. Rabelo; San Francisco State U. Presenter: Courtney Lynn McCluney; U. of Virginia Darden School of Business Presenter: Kelsie Thorne; U. of Michigan Invisible Discrimination: Divergent Implications for the Non-Prototypicality of Black Women Presenter: Rebecca Ponce de Leon; Fuqua School of Business, Duke U. “Geeky” Rules of the Game: The Effects of Nerd Masculinity on Entrepreneurial Funding Presenter: Soojin Oh; Pennsylvania State U. Presenter: Aparna Joshi; Penn State Smeal College of Business Do White Women Get Away with Racist Speech? The Effects of Identity on Perceptions of Speech Presenter: Barnini Bhattacharyya; Sauder School of Business, U. of British Columbia Presenter: Jennifer L. Berdahl; U. of British Columbia Re-Aligning Multiple Identities by Tweaking Job Advertisement Language Presenter: Joyce He; U. of Toronto Presenter: Sonia Kang; U. of Toronto
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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 | 0.014 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.030 | 0.046 |
| Scholarly communication | 0.017 | 0.009 |
| Open science | 0.002 | 0.027 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".