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Record W2578204239 · doi:10.3386/w23033

Winter is Coming: The Long-Run Effects of Climate Change on Conflict, 1400-1900

2017· preprint· en· W2578204239 on OpenAlexaff
Murat Iyigun, Nathan Nunn, Nancy Qian

Bibliographic record

VenueNational Bureau of Economic Research · 2017
Typepreprint
Languageen
FieldEarth and Planetary Sciences
TopicTree-ring climate responses
Canadian institutionsKellogg's (Canada)
Fundersnot available
KeywordsClimate changeClimatologyEnvironmental scienceHistoryMeteorologyGeographyGeologyOceanography

Abstract

fetched live from OpenAlex

We investigate the long-run effects of cooling on conflict. We construct a geo-referenced and digitized database of conflicts in Europe, North Africa, and the Near East from 1400-1900, which we merge with historical temperature data. We show that cooling is associated with increased conflict. When we allow the effects of cooling over a fifty-year period to depend on the extent of cooling during the preceding period, the effect of cooling on conflict is larger in locations that experienced earlier cooling. We interpret this as evidence that the adverse effects of climate change intensify with its duration.

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)0.003
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.160
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.276
GPT teacher head0.452
Teacher spread0.176 · 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 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

Citations38
Published2017
Admission routes1
Has abstractyes

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