Social movement strategy (nonviolent vs. violent) and the garnering of third‐party support: A meta‐analysis
Bibliographic record
Abstract
Abstract An emerging literature suggests that the success of social movements depends, partly, on their ability to garner support from third‐party groups. One factor that appears to predict support is social movements’ use of nonviolent (compared to violent) strategies to achieve their goals. However, this literature is not definitive. Herein, we report the results of a meta‐analysis of research that has assessed the effect of the use of nonviolence on third‐party support ( k = 16, N = 4598). A small‐to‐moderate positive effect was observed, d = 0.25. Additionally, research that used a control or baseline comparison group suggested that using nonviolent strategies marginally ( p = .090) increased people's willingness to help the movement ( d = 0.17) while adopting violent strategies did not increase or decrease people's willingness to help the movement ( d = −0.03). Publication bias was evidenced by bigger effect sizes of published (vs. unpublished) studies. Target (i.e., state vs. social issues) and location of the protest (i.e., domestic vs. foreign) were not significant moderators, whereas the context (i.e., real vs. hypothetical scenarios) was, although marginally. Results suggest that it behooves social movements to adopt nonviolent strategies if third‐party support is desired.
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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 | 0.017 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.038 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".