The Gender-Gap Artifact: Women's Underperformance in Quantitative Domains Through the Lens of Stereotype Threat
Bibliographic record
Abstract
Women in the traditionally masculine field of mathematics must contend with stereotypes that allege a sex-based math inability. The threat of being personally reduced to these gender stereotypes can evoke a disruptive state that undermines women's math performance – a situational predicament termed “stereotype threat.” Women are susceptible to stereotype threat whenever they risk fulfilling, or being judged by, a negative gender stereotype that provides a plausible explanation for their behavior in a given domain. This chapter examines the insidious effects that stereotype threat can have on women's performance and aspirations in all quantitative fields. Picture in your mind the typical computer scientist. Now picture the typical librarian. Is the librarian shy and the computer scientist socially awkward? Do they both wear glasses? Are they both inept at sports? Is the computer scientist a male and the librarian a female? Most people can clearly articulate the content of stereotypes targeted at various groups in our society, and gender stereotypes are no exception. As Brown and Josephs (1999) suggest, stereotypes regarding gender differences in math and science ability still pervade contemporary Western thought. In fact, if you assumed that the direction of those gender differences benefited men, you have just displayed personal knowledge of some of the negative stereotypes targeted at women in our culture. This is not meant to imply that you personally endorse those stereotypes – research has shown no relationship between personal beliefs and knowledge of stereotypes (Devine, 1989; Devine & Elliot, 1995).
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".