Does Violent Protest Backfire? Testing a Theory of Public Reactions to Activist Violence
Bibliographic record
Abstract
How do people respond to violent political protest? The authors present a theory proposing that the use of violence leads the general public to view a protest group as less reasonable, a perception that reduces identification with the group. This reduced identification in turn reduces public support for the violent group. Furthermore, the authors argue that violence also leads to more support for groups that are perceived as opposing the violent group. The authors test this theory using a large ( n = 800) Internet-based survey experiment with a politically diverse sample. Participants responded to an experimental scenario based on recent violent confrontations between white nationalist protesters and antiracist counter-protesters, allowing the authors to study whether violent protest would reduce public support even when used against a widely reviled group. The authors found that the use of violence by an antiracist group against white nationalists led to decreased support for the antiracist group and increased support for the white nationalist group. Furthermore, the results were consistent with the theorized causal process: violence led to perceptions of unreasonableness, which reduced identification with and support for the protest group. Importantly, the results revealed a striking asymmetry: although acts of violence eroded support for an antiracist group, support for white nationalist groups was not reduced by the use of violence, perhaps because the public already perceives these groups as very unreasonable and identifies with them at low levels. Consistent with this interpretation, the authors found that self-identified Republicans, a subset of the sample that reported less extremely negative views of white nationalists, showed reduced support for white nationalists when they engaged in violence.
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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 | 0.007 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".