What personality traits do citizens want politicians to have? Observational and experimental evidence of citizens' preferences in three countries
Bibliographic record
Abstract
Abstract Politicians' personality is believed to play a central role in their electoral success. It is unclear, however, how important different traits are to voters and how the impact of personality compares to that of other well‐studied individual characteristics of politicians, such as gender, age, and political experience. Drawing on evidence from three studies—an observational study ( N = 4543), a survey experiment ( N = 1031), and a preregistered conjoint experiment ( N = 4313)—conducted in Belgium, Canada, and Israel, we demonstrate that citizens value some traits (e.g., conscientiousness) more than others (e.g., extraversion) when choosing candidates. We also show that the relative effect of politicians' personality is greater than that of other individual characteristics. These results highlight the central role of elite personality in our understanding of voting behavior.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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 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".