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Record W2146164776 · doi:10.1177/0022002703258803

No Professional Soldiers, No Militarized Interstate Disputes?

2003· article· en· W2146164776 on OpenAlexaff
Seung‐Whan Choi, Patrick James

Bibliographic record

VenueJournal of Conflict Resolution · 2003
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Relations and Foreign Policy
Canadian institutionsCarleton University
Fundersnot available
KeywordsOffensiveState (computer science)Political scienceLawInternational relationsLogistic regressionSociologyPolitical economyPoliticsEconomicsManagement

Abstract

fetched live from OpenAlex

In Perpetual Peace, Immanuel Kant presents six preliminary articles for perpetual peace beforethe three well-known definitive articles about republic constitutions, commercial relations, and international organizations. In his third preliminary article, Kant argues that “Standing Armies ( miles perpetuus) Shall in Time be Totally Abolished” because they are themselves “a cause of offensive war.” Empirical results based on state-of-the-art data analysis that refers to both peace-years correction and distributed-lags logistic regression showthat the most obvious among the neglected preliminary articles by Kant—military manpower system—is indeed connected to involvement in militarized interstate disputes during the period from 1886 to 1992. For neo-Kantian peace theory and research, this means that a military manpower system with conscripted, notstanding (i.e., professional or voluntary), soldiers is associated with disputes.

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)0.005
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.015
Scholarly communication0.0050.010
Open science0.0010.002
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0120.001

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 head0.023
GPT teacher head0.333
Teacher spread0.310 · 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 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

Citations45
Published2003
Admission routes1
Has abstractyes

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