Worse for Women, Bad for All: A 62-Nation Study Confirms and Extends Ambivalent Sexism Principles to Reveal Greater Social Dysfunction in Sexist Nations
Bibliographic record
Abstract
We retested core ambivalent sexism theory tenets and explored novel correlations with national outcomes in 62 nations. Replicating Glick et al., cross-national analyses supported (a) hostile sexism (HS) and benevolent sexism (BS) as cross-culturally recognizable, complementary ideologies associated with gender inequality; (b) women appearing to be influenced by, but also resisting men’s HS and embracing BS to counter men’s HS (outscoring men in some highly sexist nations). Novel cross-national comparisons showed (a) men’s HS and both genders’ BS correlated with fewer women in paid work, whereas only BS correlated with domestic labor inequity, (b) both HS and BS correlated with accepting intimate partner violence toward women. Finally, HS and BS correlated with generally dysfunctional national outcomes: antidemocratic tendencies, less productivity, more collective violence, and lower healthy lifespan for both genders. Results reinforce that BS harms women and suggest men also have a stake in reducing sexist ideologies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".