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Record W4404831137 · doi:10.4236/tel.2024.146113

The Economics of War and Peace: Understanding Economic Incentives and Search for Sustainable Peace

2024· article· en· W4404831137 on OpenAlex
Jong Soue You

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.

Bibliographic record

VenueTheoretical Economics Letters · 2024
Typearticle
Languageen
FieldHealth Professions
TopicHealth and Conflict Studies
Canadian institutionsAlgoma University
Fundersnot available
KeywordsIncentivePeace economicsEconomicsSustainable developmentPolitical sciencePositive economicsPeace and conflict studiesNeoclassical economicsMicroeconomicsLaw

Abstract

fetched live from OpenAlex

This paper examines historical examples that strongly suggest that economic incentives are behind war and peace, particularly because economic incentives are so powerful that institutions based on such powerful incentives are virtually impossible to destroy once firmly established. This valuable historical lesson can be applied to the peaceful resolution of various regional conflicts around the world, including thorny and seemingly intractable conflicts such as the Israeli-Palestinian and Korean Peninsula conflicts. A model of multilateral international economic cooperation is suggested as a concrete example for this purpose. Understanding the nature of economic incentives and how powerful they are can lead to peaceful solutions to many of the regional conflicts presently plaguing the world.

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)0.002
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.731
Threshold uncertainty score0.795

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.043
GPT teacher head0.364
Teacher spread0.321 · 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