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Record W2603999966 · doi:10.1111/ecot.12219

Can remittances buy peace?

2019· preprint· en· W2603999966 on OpenAlexaff
Michael Batu

Bibliographic record

VenueEconomics of Transition and Institutional Change · 2019
Typepreprint
Languageen
FieldSocial Sciences
TopicInternational Development and Aid
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsRemittanceIncentiveBattleIncidence (geometry)Demographic economicsEconomicsDevelopment economicsEconomic growthGeographyMicroeconomics

Abstract

fetched live from OpenAlex

Abstract This paper offers new empirical evidence on the effects of remittance flows on conflict incidence, onset, and duration in recipient countries. It improves on previous studies by controlling for unobserved heterogeneity among remittance recipient countries, serial correlation of conflict, and the possibility of conflict being endogenous to remittances. To motivate the empirical findings, a micro‐founded model of conflict is developed. The model shows that wealth transfers (such as remittances) can raise the opportunity cost of participation in conflicts which can lead to a reduction in both the number of rebels and the force used by the government forces.

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)0.002
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.070

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0030.003
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0210.001

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 head0.062
GPT teacher head0.281
Teacher spread0.219 · 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 designObservational
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
Published2019
Admission routes1
Has abstractyes

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