Aspect-Level Personality Characteristics of U.S. Presidential Candidate Supporters in the 2016 and 2020 Elections
Bibliographic record
Abstract
With two studies ( N=1,257), we examined the aspect-level personality predictors of support for major U.S. presidential candidates in 2016 and 2020. U.S. residents completed measures of aspect-level personality, overall political orientation, and support for each candidate. The profile that predicted support for each candidate diverged from the profile that predicted generic liberalism/conservatism. Moreover, differences emerged between supporters of different candidates within the same party. For example, preference for Clinton was predicted by higher Openness, but lower Intellect, Politeness, and Volatility, whereas preference for Sanders was predicted only by higher Openness and lower Volatility. Preference for Trump was predicted by lower Openness and higher Volatility in 2016, but lower Compassion and higher Industriousness in 2020. Support for Biden was predicted by higher Compassion, Intellect, and Withdrawal. This work provides a more nuanced understanding of how the psychology of generalized political orientation may deviate from the psychology behind support for specific candidates.
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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 | 0.003 | 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 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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; both teacher heads agree on what is shown here.
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".