Bibliographic record
Abstract
This paper examines how oppositional groups go about exploiting opportunities to mobilizeen massein settings that are less than auspicious. The Green Movement is used here as a case study, the aim of which is to show that understanding how a people go about mobilizing requires, first and foremost, examining the core beliefs that motivate them toseize opportunitieswhen conditions allow. To this end, a constructivist approach will be used to demonstrate that it was the oppositional forces that took a proactive role in constructing opportunities to mobilize becausethey perceivedthe circumstances to be favorable, which suggests that greater attention ought to be focused on the sociopolitical and historical context within which a given situation is viewed as conducive to mass mobilization. Citing the examples of the student and women’s groups involved in Iran’s Green Movement, and tracing their historical trajectories and particular experiences during Ahmadinejad’s first term (2004–2008), I argue that the Green Movement may be best described as a ‘movement of movements,’ the kind of mega social movement capable of harnessing the potential, not only of Iranians but of other Middle East peoples, to mobilize with a view to pursuing specific social and political goals. This approach has the virtue of offeringa way to understandspecific traits of social movements operating in repressive settings.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.022 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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; 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".