Openness relates to COVID‐19 vaccination rates across 48 United States but politics trump personality
Bibliographic record
Abstract
Abstract Does geographic variation in personality across the United States relate to COVID‐19 vaccination rates? To answer this question, we combined multiple state‐level datasets: (a) Big Five personality averages (i.e., extraversion, agreeableness, conscientiousness, neuroticism, and openness; Rentfrow et al., 2008), (b) COVID‐19 full‐vaccination rates (CDC, 2021a), (c) health‐relevant demographic covariates (population density, per capita gross domestic product, and racial/ethnic data; Webster et al., 2021), and (d) political and religiosity data. Analyses showed openness as the strongest correlate of full‐vaccination rates ( r = 0.51). Controlling for other traits, demographic covariates, and spatial dependence, openness remained significantly related to full‐vaccination rates ( r p = 0.55). Adding political and religiosity data to this model diminished openness effects for full‐vaccination rates to non‐significance ( r p = 0.26); however, extraversion emerged as a significant correlate of full‐vaccination rates ( r p = 0.37). Although politics are paramount, we suspect that states with higher average openness scores are more conducive to novel thinking and behavior—dispositions that may be crucial in motivating people to take newly‐developed vaccines based on new technologies to confront a novel coronavirus.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".