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Record W4413339052 · doi:10.1111/ecin.70013

The economic effects of long‐term climate change: Evidence from the Little Ice Age: Replication

2025· article· en· W4413339052 on OpenAlexaff
Nikolai Cook, Hugo Cordeau, Tongzhe Li, Taylor Wright

Bibliographic record

VenueEconomic Inquiry · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicTree-ring climate responses
Canadian institutionsBrock UniversityUniversity of GuelphUniversity of TorontoWilfrid Laurier University
Fundersnot available
KeywordsReplication (statistics)Term (time)EconomicsClimate changeNatural resource economicsGeologyMedicinePhysicsOceanography

Abstract

fetched live from OpenAlex

Abstract Waldinger finds a positive relationship between temperature and city size during the climate change of 1600–1850. We show the main result differs by city size. Cities with less than 1000 inhabitants (which make up 23.5% of observations and are 49.6% of cities at some point) exhibit a strong and positive relationship between temperature and city size, whereas cities with always more than 1000 inhabitants exhibit a negative relationship. Further examination of the underlying city size data, which bins populations into coarse thousand‐wide population intervals, finds the original analysis to be robust to a number of reasonable alternative researcher choices.

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)0.025
metaresearch head score (Gemma)0.113
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Reproducibility · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.975
Threshold uncertainty score0.147

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0250.113
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.004
Science and technology studies0.0020.003
Scholarly communication0.0020.001
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0110.002

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 head0.044
GPT teacher head0.295
Teacher spread0.251 · 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.

Study designSimulation or modeling
DomainReproducibility
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
Published2025
Admission routes1
Has abstractyes

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