The activist’s dilemma: Extreme protest actions reduce popular support for social movements.
Bibliographic record
Abstract
How do protest actions impact public support for social movements? Here we test the claim that extreme protest actions-protest behaviors perceived to be harmful to others, highly disruptive, or both-typically reduce support for social movements. Across 6 experiments, including 3 that were preregistered, participants indicated less support for social movements that used more extreme protest actions. This result obtained across a variety of movements (e.g., animal rights, anti-Trump, anti-abortion) and extreme protest actions (e.g., blocking highways, vandalizing property). Further, in 5 of 6 studies, negative reactions to extreme protest actions also led participants to support the movement's central cause less, and these effects were largely independent of individuals' prior ideology or views on the issue. In all studies we found effects were driven by diminished social identification with the movement. In Studies 4-6, serial mediation analyses detailed a more in-depth model: observers viewed extreme protest actions to be immoral, reducing observers' emotional connection to the movement and, in turn, reducing identification with and support for the movement. Taken together with prior research showing that extreme protest actions can be effective for applying pressure to institutions and raising awareness of movements, these findings suggest an activist's dilemma, in which the same protest actions that may offer certain benefits are also likely to undermine popular support for social movements. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".