Sex differences in HEXACO personality characteristics across countries and ethnicities
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it