Does Stereotype Threat Affect Men in Language Domains?
Bibliographic record
Abstract
Currently, boys and men tend to underperform in language domains in school and on standardized tests, and they are also underrepresented in language-related fields of study. Stereotype threat is one way in which psychologists have found that stereotypes can affect students’ performance and sense of belonging in academic subjects and test settings Researchers have traditionally found that when girls and women are reminded of stereotypes that math is for boys, girls and women tend to underperform on math tests. Stereotype threat has also been found to affect women’s sense of belonging in math settings. We sought to test whether stereotypes that women have better language skills than men would affect men in the same way. We conducted a series of four experiments (N=542) testing the effect of explicit stereotype threats on men’s performance in language-related tasks, and their sense of belonging to language-related domains. We found little evidence for stereotype threat effects on men in language tasks. Mini-meta analyses revealed aggregate effect sizes indistinguishable from zero across our studies, and Bayesian analysis suggested that the null hypothesis was consistently more likely than the alternative. Future research should explore other explanations for gender gaps in language.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".