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Record W4311719715 · doi:10.1177/00207152221140344

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

2022· article· en· W4311719715 on OpenAlexvenueno aff
Eitan Y. Alimi

Bibliographic record

VenueInternational Journal of Comparative Sociology · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicPolitical Conflict and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsEmbeddednessPoliticsAuthoritarianismPolitical economyState (computer science)SovereigntyProcess tracingSociologyPolitical scienceEconomic systemEconomicsLawSocial scienceDemocracy

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.272
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.004
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.134
GPT teacher head0.371
Teacher spread0.238 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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