The Montagu Principle: Incivility decreases politicians' public approval, even with their political base
Bibliographic record
Abstract
M. W. Montagu asserted that, “civility costs nothing and buys everything.” In the realm of social judgment, the notion that people generally evaluate civil people more favorably than uncivil people may be unsurprising. However, the Montagu Principle may not apply in a hyper-partisan political environment in which politicians “throw red meat to their base” by unleashing uncivil, personal attacks against their opponents, satisfying the aggressive desires of their most hyper- partisan supporters, and thus potentially redoubling their approval among them. We conducted two longitudinal/observational studies of U.S. Congress and President Trump, and 4 experiments (N = 4837) involving real exchanges between President Trump and his adversaries and a speech by a fictitious politician. Civility helped or did not affect—but never harmed—the reputation of the speaker, supporting the Montagu Principle. Even self-identified “diehard supporters” of President Trump, for example, evaluated the president more favorably after he responded with civility to a personal attack. Uncivil remarks uniquely diminished the speaker’s reputation, and had little impact on the reputation of the targets of the attack, the perceived winner of the verbal exchange, the reputation of the speaker’s party, or the sense that the country is moving in the right direction. Incivility made the speaker seem less warm and did less to affect perceptions of dominance or honesty. This warmth deficit explained the reputational costs of incivility.
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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 | 0.010 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".