Personality, Religion, and Politics: An Investigation in 33 Countries
Bibliographic record
Abstract
The relations of HEXACO personality factors and religiosity with political orientation were examined in responses collected online from participants in 33 countries ( N = 141 492). Endorsement of a right–wing political orientation was negatively associated with Honesty–Humility and Openness to Experience and positively associated with religiosity. The strength of these associations varied widely across countries, such that the religiosity–politics correlations were stronger in more religious countries, whereas the personality–politics correlations were stronger in more developed countries. We also investigated the utility of the narrower traits (i.e. facets) that define the HEXACO factors. The Altruism facet (interstitially located between the Honesty–Humility, Agreeableness, and Emotionality axes) was negatively associated with right–wing political orientation, but religiosity was found to suppress this relationship, especially in religious countries. In addition to Altruism, the Greed Avoidance and Modesty facets of the Honesty–Humility factor and the Unconventionality and Aesthetic Appreciation facets of the Openness to Experience factor were also negatively associated with right–wing political orientation. We discuss the utility of examining facet–level personality traits, along with religiosity, in research on the individual difference correlates of political orientation. Copyright © 2018 European Association of Personality Psychology
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".