Incivility Diminishes Interest in What Politicians Have to Say
Bibliographic record
Abstract
Incivility is prevalent in society suggesting a potential benefit. Within politics, theorists and strategists often claim incivility grabs attention and stokes interest in what a politician has to say. In contrast, we propose incivility diminishes overall interest in what a politician has to say because people find the incivility morally distasteful. Studies 1a and 1b examined the relationship between uncivil language and followership in the Twitter feeds of Presidents Donald Trump and Joe Biden, finding incivility reduced their following on the platform. In Studies 2–3, we manipulated how uncivil a number of politicians were and found that incivility consistently depressed interest in what they had to say. These effects of incivility are generalized to both political allies and opponents. Observers’ moral disapproval of the incivility mediated the diminished interest, suppressing the attention-grabbing nature of incivility. Altogether, our findings indicate that the public reacts more negatively to political incivility than previously thought.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.222 | 0.005 |
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; both teacher heads agree on what is shown here.
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".