Failure Leads Protest Movements to Support More Radical Tactics
Bibliographic record
Abstract
Most social movements will encounter setbacks in their pursuit of sociopolitical change. However, little is known about how movements are affected after protestors fail to achieve their aims. What are the effects of failure on subsequent engagement in various conventional and radical actions? Does failure promote divergent reactions among protestors and/or dissatisfaction with democracy? A meta-analysis of nine experiments ( N = 1,663) assessed the effects of one-off failure on protestors’ reactions, subsequent tactical choices, and support for democracy; and iterative stochastic simulations modeled the effects of failure over multiple protests over time. Results indicated that initial failure gives rise to divergent, somewhat contradictory responses among protestors and that these responses are further influenced by the repeated failure (vs. success) over time. Further, the simulations identified “tipping points” in these responses that promote radicalization and undermine support for democracy.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".