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Record W2485938141 · doi:10.1080/13698249.2016.1205561

Politics in the shadow of the gun: revisiting the literature on ‘Rebel-to-Party Transformations’ through the case of Burundi

2016· article· en· W2485938141 on OpenAlexafffund
Katrin Wittig

Bibliographic record

VenueCivil Wars · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicPolitical Conflict and Governance
Canadian institutionsUniversité de MontréalCanadian Institute for International Peace and Security
FundersPierre Elliott Trudeau Foundation
KeywordsPoliticsAuthoritarianismScholarshipShadow (psychology)Political sciencePolitical violencePolitical economySociologyArmed conflictLawDemocracy

Abstract

fetched live from OpenAlex

This article provides a critical review of ‘rebel-to-party transformation’ scholarship. It shows how three flawed assumptions have underpinned much of the literature: (1) an ideal-typical differentiation between rebel group and political party as distinct by their use or rejection of violence; (2) the analysis of armed conflict as breakdown of ‘normal’ politics, and the study of ‘rebel-to-party conversions’ as a gradual, natural shift from violence back to politics; (3) a failure to integrate the study of rebel legacies into an examination of broader authoritarian legacies. These assumptions have clouded our understanding of politico-military organizations in conflict-torn societies, which combine social protest, armed rebellion, political violence, and party politics throughout their history. Drawing on the ‘no peace, no war’ and ‘armed politics’ paradigms, this article revisits these assumptions through the case of Burundi.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.028
Threshold uncertainty score0.089

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.003
Science and technology studies0.0170.035
Scholarly communication0.0080.008
Open science0.0010.008
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.0040.000

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 head0.025
GPT teacher head0.313
Teacher spread0.288 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations55
Published2016
Admission routes2
Has abstractyes

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