Autonomous and/or institutionalized social movements? Conceptual clarification and illustrative cases
Bibliographic record
Abstract
Case studies of urban squatting in the United States and The Netherlands, and the fight against sexual violence in Spain and in The Netherlands, form the empirical basis of an analysis of the features and development of autonomous and institutionalized social movements, and the interaction between them. Autonomous and institutionalized social movements have different strengths that they derive from characteristics that are not compatible. Nevertheless, a dynamic is possible that combines the strengths of both models. It provides synergy between self-contained autonomous and institutionalized movements, without imposing trade-offs. Political opportunity theory suggests that such a ‘dual movement structure’ is most relevant when the political system is selectively open. Interaction between the movements is conditioned by the mainstreaming potential of the issue or interest that is at stake. Even when relations are tense, movements can create opportunities for each other. Autonomous movements are able to retain a repertoire of disruptive actions when lobbying is the more popular option. An autonomous movement can benefit from the legitimacy and supporting network engendered by an institutionalized movement, pioneering work done by an autonomous movement can inspire an institutionalized counterpart. Autonomous movements can provide a critical voice when co-optation occurs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.020 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".