Virtues, Vices, and Political Influence in the U.S. Senate
Bibliographic record
Abstract
What qualities make a political leader more influential or less influential? Philosophers, political scientists, and psychologists have puzzled over this question, positing two opposing routes to political power--one driven by human virtues, such as courage and wisdom, and the other driven by vices, such as Machiavellianism and psychopathy. By coding nonverbal behaviors displayed in political speeches, we assessed the virtues and vices of 151 U.S. senators. We found that virtuous senators became more influential after they assumed leadership roles, whereas senators who displayed behaviors consistent with vices--particularly psychopathy--became no more influential or even less influential after they assumed leadership roles. Our results inform a long-standing debate about the role of morality and ethics in leadership and have important implications for electing effective government officials. Citizens would be wise to consider a candidate's virtue in casting their votes, which might increase the likelihood that elected officials will have genuine concern for their constituents and simultaneously promote cooperation and progress in government.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".