Are partisans divided on the virtuous aspiration for political office? A study on the moderating role of ideology on the pathway from personality traits to political ambition
Bibliographic record
Abstract
We study (non)virtuous traits of individuals with nascent ambition, and whether right-wing ideology increases the chances of non-virtuous people aiming to enter politics. Right-wing voters tend to prefer candidates with darker personality traits than centrist/left-wing voters. However, not much is known about whether the self-selection of citizen candidates, i.e., the supply of (non)virtuous (potential) political candidates, matches these preferences. This is because extant research mainly focusses on the main effects of personality traits on nascent ambition, according to which non-virtuous citizens show higher nascent ambition than virtuous ones. To fill this gap, we use data from four democracies—Denmark, the Netherlands, Switzerland, and Canada—from the University of Antwerp-based project “How politicians evaluate public opinion” (POLPOP II). Using the brief-HEXACO inventory (specifically, honesty-humility personality trait) to measure presence of virtuous traits, we run moderated logistic regressions to identify whether ascribing to right-wing ideology increases the propensity of dishonest individuals’ aspiration for political office.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".