MétaCan
Menu
Back to cohort
Record W2022253944 · doi:10.2202/1554-8597.1197

War! What Is It Good For? A Deep Determinants Analysis of the Cost of Interstate Conflict

2010· article· en· W2022253944 on OpenAlex
Steven Yamarik, Noel D. Johnson, Ryan A. Compton

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

VenuePeace Economics Peace Science and Public Policy · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicCulture, Economy, and Development Studies
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsEndogeneityPer capitaEconomicsStandard deviationReal gross domestic productGross domestic productEconometricsRegression analysisInstrumental variablePer capita incomeDemographic economicsDevelopment economicsStatisticsMacroeconomicsDemographyMathematics

Abstract

fetched live from OpenAlex

Whatever gains may come from fighting wars, economic growth is not among them. We examine the long-run impact of interstate conflict on real GDP per capita for a cross section of countries between 1960 and 2000. We construct a fatality-weighted conflict variable that accounts for both the severity and endogeneity of individual confrontations. We include our conflict measure in a deep determinants income regression in which we control for trade, institutions and geography. We find that a standard deviation increase in fatality-weighted conflict over the period 1960 to 2000 results in an average decrease of about a tenth of a standard deviation in 2000 real GDP per capita.

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)0.001
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 score0.746
Threshold uncertainty score0.971

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.046
GPT teacher head0.338
Teacher spread0.292 · 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