Bibliographic record
Abstract
What role does religion play in mobilization in general, and in mobilization in conflict settings in particular? This chapter explores these questions through a case study of the Syrian uprising and war. Using published sources and original interviews, the author traces the role of religion and sect in Syria’s pre-2011 politics and then in successive stages of the subsequent conflict. She examines the role of religion in the motivations driving protest, the processes generating collective action, the militarization of mobilization, and the transformation of an uprising into war. It is argued that, while religion came to occupy an increasingly prominent place in mobilization over time, its role in the Syrian conflict has been less attributable to religion per se than to the ways religion is entwined with power, privilege, and the dynamics of violence itself. Where religion sometimes appeared significant, such as in the tendency of demonstrations to begin at mosques, the power of religion lay not in piety but in structural constraints. Though religion and sect became increasingly salient as the conflict escalated, this was primarily due to state repression and strategies of divide and conquer, and nothing particular to Islam. Scrutiny of the Syrian experience encourages us to critique assumptions about the distinctiveness of religion in driving protest and conflict in majority-Muslim societies, and instead to examine such mobilization using the same conceptual tools employed in cases of conflict across time and space.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.026 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| 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".