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Record W4411520023 · doi:10.31219/osf.io/cfrzs_v1

Cities, Conflict, and Corridors

2021· preprint· en· W4411520023 on OpenAlexfundno aff
Shuhei Kitamura, Nils‐Petter Lagerlöf

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicCrime, Illicit Activities, and Governance
Canadian institutionsnot available
FundersJapan Society for the Promotion of ScienceSocial Sciences and Humanities Research Council of Canada
KeywordsGeographyFragmentation (computing)BelligerentEconomic geographyMultitudeState (computer science)Political geographyGeographical distancePolitical scienceSociologyDemographyLawEcologyMathematics

Abstract

fetched live from OpenAlex

In this paper, we link European state fragmentation to geography, conflict, and the locations of capitals. First we document that military battles tend to occur close to the shortest-distance corridors between the capitals of the belligerent powers, *except* where that corridor is intercepted by certain types of geography, specifically seas, mountains, and marshes. Geography thus seems to have influenced the effective military distance between the belligerents' capitals. Then we explore similar corridors between a multitude of European cities, documenting two patterns: (1) capitals tend to be closer to each other when the geography between them is more separating, as measured by similar types of geography found to affect battle locations; (2) controlling for distance, the likelihood that any two cities are located in the same state decreases with the same types of geography between them. We present a model consistent with these patterns.

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)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.014
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.005
Science and technology studies0.0010.003
Scholarly communication0.0030.003
Open science0.0000.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0080.000

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 head0.048
GPT teacher head0.314
Teacher spread0.266 · 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 designNot applicable
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
Published2021
Admission routes1
Has abstractyes

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