Hostile, Benevolent, Implicit: How Different Shades of Sexism Impact Gendered Policy Attitudes
Bibliographic record
Abstract
Advances in gender equality and progressive policies are often stymied by cultural sexist systems and individual-level sexist attitudes. These attitudes are pervasive but vary in type—from benevolent to hostile and implicit to explicit. Understanding the types of sexism and their foundations are important for identifying connections to specific social and political attitudes and behaviors. The current study examines the impact of various manifestations of sexism on attitudes regarding policies and public opinion issues that involve gender equality or have gendered implications. More specifically, we look at attitudes on reproductive rights, support for the #MeToo Movement, equal pay, and paid leave policies. In Study 1 we use data from a high-quality web panel ( n = 1,400) to look at the relationship between hostile, benevolent, and implicit sexism, and reproductive rights attitudes, as well as support for the #MeToo Movement. In Study 2 we use data from the American National Election Study ( n = 4,270) to examine the relationship between hostile and modern sexism and attitudes on abortion, equal pay, and paid family leave. Overall, these results reveal a complicated relationship between different conceptualizations of sexism and gendered attitudes, underscoring the need to consider how different forms of sexism shape broader social and political views, from both a normative perspective for societal change and a measurement approach for research precision.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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".