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 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 | 0.001 | 0.003 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".