Too honest and humble to run for office? Citizens’ personality traits, nascent ambition, and recruitment
Bibliographic record
Abstract
Abstract We explore how honesty-humility and the other HEXACO personality traits relate to citizens’ nascent ambition and their recruitment to run for office. We extend previous work on virtue-related personality traits and political recruitment in two important ways: we go beyond North America and conduct a five-country cross-national study with nationally representative samples. More importantly, going beyond individual-level differences in nascent ambition, we also address how honesty-humility predicts the likelihood of being asked to and actually running for office. Based on data from Canada, Denmark, Israel, the Netherlands, and Switzerland, we demonstrate that citizens with lower levels of honesty-humility are more likely to have considered running, to deem themselves qualified to run, to have been asked to run, and to actually have run for a political office. From a ‘virtue ethics’ perspective, this is highly concerning: low honesty-humility predisposes individuals to engage in unethical behavior and decision-making. We discuss implications for the quality of political representation.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.003 | 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 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".