The compassionate sexist? How benevolent sexism promotes and undermines gender equality in the workplace.
Bibliographic record
Abstract
Although sexist attitudes are generally thought to undermine support for employment equity (EE) policies supporting women, we argue that the effects of benevolent sexism are more complex. Across 4 studies, we extend the ambivalent sexism literature by examining both the positive and the negative effects benevolent sexism has for the support of gender-based EE policies. On the positive side, we show that individuals who endorse benevolent sexist attitudes on trait measures of sexism (Study 1) and individuals primed with benevolent sexist attitudes (Study 2) are more likely to support an EE policy, and that this effect is mediated by feelings of compassion. On the negative side, we find that this support extends only to EE policies that promote the hiring of women in feminine, and not in masculine, positions (Study 3 and 4). Thus, while benevolent sexism may appear to promote gender equality, it subtly undermines it by contributing to occupational gender segregation and leading to inaction in promoting women in positions in which they are underrepresented (i.e., masculine positions). (PsycINFO Database Record
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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 | 0.007 |
| 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 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".