Does political violence backfire in mature democracies? Evidence from the Capitol insurrection in the USA
Bibliographic record
Abstract
Abstract Does political violence around election times decrease support for political elites associated with violent actions? We address this question in the understudied context of a mature democracy, where established electoral processes, effective accountability mechanisms, and a vibrant civil society are likely to reduce the appeal of violence. In this context, we hypothesize that political violence during election periods decreases support for political elites who propagate or condone such actions. To test this hypothesis, we examine the impact of the Capitol insurrection on support for the Republican and Democratic parties in the United States. Specifically, we analyze tweets posted by members of the US Congress around the time of the insurrection and use social media engagement as an indicator of public support for both parties. Employing a series of short-run difference-in-differences models, we find that the Capitol attack reduced engagement with messages posted by Republican politicians compared to Democrats. This effect is especially pronounced for Republican politicians closely aligned with Donald Trump, who is widely seen as having incited the attack. Importantly, our findings are not driven by the general negativity of Republican tweets or their explicit attacks on the Democratic Party, both of which could plausibly have heightened tensions. Instead, the evidence supports a ‘blame attribution’ mechanism, wherein the public punishes politicians responsible for instigating violence or condoning those who do. These results are robust to a series of falsification and permutation tests and cannot be explained by attrition following Twitter’s bans on radical users. We find evidence suggestive of the long-term consequences of these patterns for electoral outcomes.
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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 | 0.009 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".