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Record W4283792145 · doi:10.1177/00220027221112030

State breakdown and Army-Splinter Rebellions

2022· article· en· W4283792145 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Conflict Resolution · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicPolitical Conflict and Governance
Canadian institutionsUniversité de Montréal
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsSuperpowerState (computer science)Spanish Civil WarPolitical economyCold warFellPolitical scienceHeading (navigation)Regime changeMobilizationDevelopment economicsLawEconomic historyHistorySociologyGeographyPoliticsEconomicsDemocracyCartography

Abstract

fetched live from OpenAlex

In Afghanistan, Libya, Liberia and beyond, armed rebellions have begun when armies fell apart. When does this occur? This paper conducts a large-N analysis of these army-splinter rebellions, distinct from both non-military rebellions from below and from coups, using new data. It finds that they follow a logic of state breakdown focusing on regime characteristics (personalist regimes and the loss of superpower support at the end of the Cold War) rather than drivers of mass mobilization from below. In contrast, these regime-level factors matter much less for the non-military rebellions from below that dominate theorizing about civil war origins. This paper also shows that one option for military rebels lies in not attempting a coup but instead heading straight into a rebellion. This paper thus distinguishes highly different paths to armed conflict, validates the state breakdown approach to why armies fall apart, and extends the well-known tradeoff between coups and civil wars.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.942
Threshold uncertainty score0.410

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 studies0.0010.000
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 head0.026
GPT teacher head0.312
Teacher spread0.285 · 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