Sex differences in HEXACO personality characteristics across countries and ethnicities
Bibliographic record
Abstract
OBJECTIVE: We examined sex differences in the HEXACO Personality Inventory-Revised (HEXACO-PI-R) factor- and facet-level scales and the associations of national sex differences in those scales with national characteristics such as wealth and gender equality. METHOD: HEXACO-PI-R self-reports were collected online from persons in 48 countries (N = 347,192). RESULTS: (1) Women averaged substantially higher than men in Emotionality and in Honesty-Humility, with (sample-unweighted) mean differences across countries of d = 0.84 and d = 0.37, respectively; (2) the HEXACO-PI-R factor scales showed a rather large multivariate sex difference (D > 1 in most countries), about 19% larger than found in similar samples with the Big Five personality factors, (3) some facet scales belonging to the same factor showed widely varying sex differences, (4) national-level sex differences in Emotionality were larger in wealthy and gender-egalitarian countries, replicating previous counterintuitive findings, but such a tendency was not clearly observed for Honesty-Humility, and (5) within several English-speaking countries, sex differences in Emotionality showed comparatively little ethnic variation, suggesting that societal characteristics may influence the size of sex differences in Emotionality. CONCLUSION: The HEXACO model of personality structure provides some new insights in understanding sex differences in personality at the individual and national levels.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".