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Record W4220871438 · doi:10.1177/13540661221084870

Committed sponsors: external support overtness and civilian targeting in civil wars

2022· article· en· W4220871438 on OpenAlexaff
Arthur Stein

Bibliographic record

VenueEuropean Journal of International Relations · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicPolitical Conflict and Governance
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsCovertPoliticsIncentivePolitical economyState (computer science)Political scienceSpanish Civil WarAffect (linguistics)Public supportLaw and economicsLawSocial psychologyPublic relationsSociologyPsychologyEconomicsMarket economy

Abstract

fetched live from OpenAlex

Does the overtness of external support to rebels affect civilian targeting in civil wars? Conflict studies increasingly scrutinize how insurgent sponsorships shape rebels’ behavior. However, the influence of external sponsors’ decisions to publicly acknowledge or deny their support on rebel conduct is largely neglected. This article introduces a new dataset on the overtness of external support to rebels in civil wars between 1989 and 2018. It then assesses whether the overtness of support is correlated with insurgents’ propensity to target civilians. I hypothesize that overtly supported rebels are less likely to target civilians than covertly supported rebels. This hypothesis stems from how supply-side factors—the way state sponsors expectedly act after having allocated their support—impact insurgents’ structure of incentives around relations with non-combatants. Statistical analyses yield strong support for my hypothesis. Moreover, further analyses show that support overtness influences civilian targeting independently from sponsors’ characteristics, such as political regimes or foreign aid reliance. Thus, in addition to the type of material aid insurgents receive, variation in whether support is covert or overt shapes how rebels treat civilians.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.699
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.017
GPT teacher head0.287
Teacher spread0.270 · 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 designNot applicable
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

Citations4
Published2022
Admission routes1
Has abstractyes

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