Just a Few Bad Apples: Perceiving Sexist Men as Non‐Typical to the Larger Category of Men Serves to Maintain the Gender Hierarchy
Bibliographic record
Abstract
ABSTRACT Social problems, including those related to gender discrimination, are often simplistically attributed to a few ‘bad apples’ rather than systemic issues. This research explores one aspect of this under‐investigated phenomenon by focusing on women's perceptions of sexist men. Three pre‐registered correlational studies ( n = 647) explored whether women's tendency to perceive sexist men as non‐typical (‘bad apples’) versus typical of the larger category of men is associated with benefits on intrapersonal and interpersonal levels but with costs on an intergroup level. At the intrapersonal and interpersonal levels, perceiving sexist men as non‐typical was associated with a stronger feeling of well‐being, more positive perceptions of men, and stronger social connectedness with men. However, at the intergroup level, it was associated with lower intentions to engage in collective action on behalf of women's issues. These findings suggest that perceiving the subgroup of sexist men as non‐typical of the larger category of men is a perception that may contribute to maintaining the gender status quo. The societal and practical implications of this research are elaborated in the accompanying social impact statement. Please refer to the section to find this article's Community and Social Impact Statement.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".