Personality-Politician Conjoint Experiment
Bibliographic record
Abstract
Which traits affect a politician's success and electability? Scholars have examined several attributes such as gender, race, or social class (e.g., Carnes and Lupu 2016; Hainmueller et al. 2014; Hobolt and Rodon 2020; Schwarz and Coppock 2020). To a lesser extent, personal characteristics have been examined. For instance, Druckman and colleagues (2004) investigate the effect of competence and sociability. Personality has also been in the center of literature in psychology and political science to understand how specific traits can influence citizens' preferences (e.g., Aichholzer and Willmann 2020; Caprara et al. 2003; Caprara and Zimbardo 2004, Nai et al. 2021). In this project, we analyze the effect of politician's personality on voters' preferences and attitudes. The design is built around the findings from the first part of the paper (observational study). Using a unique dataset with personality measures of citizens and incumbent politicians in Belgium, Canada, and Israel, we find that citizens prefer politicians with a high score for conscientiousness and emotional stability. Further, we observe that citizens tend to prefer politicians that are similar to them in terms of personality. Finally, we show that citizens' personality preferences are strongly similar to politicians' actual personalities. This experiment aims to complement these results by looking at the comparative effect of traits. We designed a conjoint analysis in Canada, Israel, and Belgium to test whether a party leader's personality can influence the voter's choice and preferences.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".