Yet another dark side of chivalry: Benevolent sexism undermines and hostile sexism motivates collective action for social change.
Bibliographic record
Abstract
The current research tests a model for understanding how benevolent sexism undermines, whereas hostile sexism promotes, social change. Study 1 (N = 99) and Study 2 (N = 92) demonstrate that exposing women to benevolent sexism decreases their engagement in collective action, whereas exposure to hostile sexism increases it. Both effects were mediated by gender-specific system justification and perceived advantages of being a woman. In Study 2, positive and negative affect also mediated these relationships. Results from Studies 3 and 4 (N = 68 and N = 37) support the causal chain described in the mediational models tested in Studies 1 and 2. Manipulations that increased gender-specific system justification (Study 3) and perceived advantages of being a woman (Study 4) reduced intentions to participate in collective action.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".