Revolutionary struggle and its diffusion: A configurational analysis of the 2011 Arab Uprisings
Bibliographic record
Abstract
Researchers of revolutionary waves argue that early cases diffuse mobilization to later cases which are, compared with their forerunners, disadvantaged as they have fewer favorable antecedent conditions and less strategic protagonists. Using fuzzy-set Qualitative Comparative Analysis (fsQCA) and comparative case studies, I examine the 2011 Arab Uprisings in order to ask: (1) Why does revolutionary struggle (mass mobilization to topple an existing regime) in a given region emerge in and then diffuse to some countries but not others? and (2) Why do the struggles vary in form—that is, in terms of social composition, action types, and demands? My study finds three configurations of antecedent conditions that favor the emergence of revolutionary struggle. The three “paths” follow a two-sided narrowing pattern: each successive path has fewer expected favorable conditions and yields a less expansive form of struggle. These paths account for the six national cases of “revolutionary struggle” in the region, and co-occur with one path leading to mass reformist struggle and several other paths leading to relative quiescence. Overall, the article demonstrates how specific antecedent conditions combine in causal ways amid the temporal unfolding of a revolutionary wave. Furthermore, by identifying the narrowing pattern of revolutionary diffusion, the article suggests that later struggles essentially reflect locally informed strategic rationales, not irrational emulation or externally driven emergent processes, as posited by previous studies.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".