The Volatility of Collective Action: Theoretical Analysis and Empirical Data
Bibliographic record
Abstract
Collective action is volatile: characterized by swift, unexpected changes in intensity, target, and forms. We conduct a detailed exploration of four reasons that these changes occur. First, action is about identities which are fluid, contested, and multifaceted. As the content of groups’ identities change, so do the specific norms for the identities. Second, social movements adopt new tactics, or forms of collective action. Tactical changes may arise from changes in identity, but also changes in the target or opponent groups, and changes in the relationships with targets and with other actors. Factions or wings of a group in conflict may in turn form identities based on opposition or support for differing tactics. Third, social movements change because participant motivation ebbs, surges, and also changes in quality (e.g., becoming more subjectively autonomous, or self‐determined). Finally, political social change occurs within socio‐political structures; these structures implicate higher‐level norms, which both constrain and emerge from actions (e.g., state openness or repression). Our analysis presents idealized and descriptive models of these relationships, and a new model to examine tactical changes empirically, the DIME model. This model highlights that collective actors can D isidentify after failure (giving up and walking away); they can I nnovate, or try something new; and they can commit harder, convinced that they are right, with increased moral urgency ( M oralization) and redoubled efforts ( E nergization).
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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 | 0.010 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".