Religiousness and the HEXACO personality factors and facets in a large online sample
Bibliographic record
Abstract
OBJECTIVE: To examine the associations of religiousness with personality characteristics. METHOD: We obtained self-ratings of religiousness along with self-reports on the HEXACO Personality Inventory-Revised in a sample of nearly 200,000 online respondents. Respondents also indicated their religious affiliation, religiousness of upbringing, and political orientation; a subset of the respondents also indicated attitudes about immigration and foreign aid. RESULTS: Religiousness showed weak associations (|r|s < 0.15) with several HEXACO factors but somewhat stronger associations with the Fairness and Altruism facets (both rs > 0.20). On those facets, participants with the highest religiousness self-rating (7 on a 1-to-7 scale) averaged about 1 SD higher than did participants with the lowest religiousness self-rating. In addition, religiousness was negatively related to the Unconventionality facet among persons whose upbringing was very religious, but not among persons whose upbringing was very nonreligious. Religiousness/personality associations were generally quite similar within different religious affiliations (Hinduism, Islam, Judaism, Buddhism, and several branches of Christianity) but were stronger within relatively religious countries than within nonreligious countries. Despite the positive association of religiousness with the Altruism facet, religiousness was uncorrelated with pro-out-group attitudes (i.e., favoring multicultural immigration and foreign aid). CONCLUSION: The findings advance our understanding of religiousness/personality associations.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".