MétaCan
Menu
Back to cohort
Record W4406250146 · doi:10.1177/19485506241302882

Worse for Women, Bad for All: A 62-Nation Study Confirms and Extends Ambivalent Sexism Principles to Reveal Greater Social Dysfunction in Sexist Nations

2025· article· en· W4406250146 on OpenAlexaff
Magdalena Zawisza, Natasza Kosakowska‐Berezecka, Peter Glick, Michał Olech, Tomasz Besta, Paweł Jurek, Jurand Sobiecki, Deborah L. Best, Jennifer K. Bosson, Joseph A. Vandello, Saba Safdar, Anna Włodarczyk, Magdalena Żadkowska

Bibliographic record

VenueSocial Psychological and Personality Science · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicSocial and Intergroup Psychology
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsIdeologyPsychologyAmbivalenceDysfunctional familySocial psychologySystem justificationInequalityGender studiesDemographySociologyPoliticsClinical psychologyPolitical science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.792
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.168
GPT teacher head0.461
Teacher spread0.293 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations17
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueSocial Psychological and Personality ScienceSame topicSocial and Intergroup PsychologyFrench-language works237,207