Why don’t “real men” learn languages? Masculinity threat and gender ideology suppress men’s language learning motivation
Bibliographic record
Abstract
Large gender disparities in participation still exist across many university subjects and career fields, but few studies have examined factors that account for gender gaps in female-dominated disciplines. We examine one possible cause: threatened masculinity among men who hold traditional gender ideologies. Past research has linked endorsement of traditional gender ideologies to gender-stereotypical occupational choices, and threats to masculinity can lead men to distance themselves from femininity. After confirming that 1,672 undergraduates stereotyped language learning as feminine, we applied a masculinity threat manipulation to investigate 182 men’s disinterest in studying foreign languages, a female-dominated university subject. Men with traditional masculinity ideologies reported less interest in foreign language study and less positive attitudes towards foreign languages following masculinity threat, compared to men whose masculinity was affirmed or who held less traditional masculinity beliefs. Traditional masculine gender roles may lead some men to avoid feminine-typed domains, such as foreign language learning.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".