Beyond Openness to Experience and Conscientiousness: Testing links between lower‐level personality traits and American political orientation
Bibliographic record
Abstract
INTRODUCTION: Research has consistently revealed positive correlations between political liberalism and Openness to Experience, and between conservatism and Conscientiousness. Most of this research has made use of domain-level models of the Big Five personality traits. Recent work suggests, however, that each Big Five trait domain can be divided into distinct aspects or facets, which offer more nuanced characterizations of each trait. METHODS: Across four studies (Ns ranging from 1,123 to 116,406), the present research examined the degree to which distinct lower-level traits would be associated with meaningful differences in political orientation. United States residents completed two different hierarchical Big Five personality measures (the Big Five Aspect Scales and the Big Five Inventory-2), as well as a range of measures of political orientation. RESULTS: Across both personality measures, liberal political orientation showed distinct positive associations with the lower-level traits Openness/Aesthetic Sensitivity, Intellect/Intellectual Curiosity, Compassion, and Withdrawal/Depression, as well as distinct negative associations with Orderliness/Organization, Politeness, and Assertiveness. DISCUSSION: By examining individual differences at a higher level of granularity, these data provide insight into specific motivations that predispose individuals toward different ends of the political spectrum.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".