Army embeddedness, political opportunities and threats, and the dynamics of contention: Understanding the varying role of the armed forces in the Egyptian, Syrian, and Libyan 2011 revolts
Bibliographic record
Abstract
In many Middle East and North Africa (MENA) countries, the army has traditionally been a central pillar of the authoritarian regimes, responsible for the security and integrity of the state and a symbol of national sovereignty and social unity. Nevertheless, the 2011 Arab revolts witnessed stark differences in the response of the armies. This article argues that a relational reading of the Structure of Political Opportunities and Threats, particularly when its dimension of the state’s capacity and propensity for repression is informed by a MENA-salient regime feature—army embeddedness—offers a compelling solution to the puzzle. An analysis of the Egyptian, Syrian, and Libyan episodes of contention, based on a comparative method that combines mechanism-based process tracing and typological theorizing, demonstrates the theoretical payoffs of this sensitized dimension. Cross-case similarities underscore the value of thinking about the army as a full-fledge agent embedded within a web of relations with social and political forces. Specifically, findings reveal how army embeddedness shapes the respective operation and effect of the mechanisms “political opportunities” and “political threats,” and highlight the importance of differentiating between the state’s capacity and the state’s propensity for repression. Within-case variations highlight the historically specific development of such embeddedness and how it plays out distinctively in each case, forming different scenarios of high and low capacity and propensity for repression.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.006 | 0.022 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".