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Record W2022784591 · doi:10.1111/0162-895x.00341

Worldviews in Foreign Policy: Realism, Liberalism, and External Conflict

2003· article· en· W2022784591 on OpenAlexaboutno aff
Thomas S. Mowle

Bibliographic record

VenuePolitical Psychology · 2003
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Relations and Foreign Policy
Canadian institutionsnot available
Fundersnot available
KeywordsForeign policyRealismLiberalismSituational ethicsInternational relationsInternational relations theoryPositive economicsSocial psychologyInternational conflictState (computer science)Political sciencePolitical economySociologyEpistemologyPsychologyLawPoliticsEconomics

Abstract

fetched live from OpenAlex

International relations studies have been unable to determine whether realist or liberal theories better fit state behavior in various situations, possibly because these studies have attributed motive and action to the states rather than to the decision‐makers within them. This article develops a new, more direct approach to resolving this problem. Hypotheses were tested regarding conditions under which decision‐makers are likely to articulate a problem representation consistent with liberal or realist elements of a worldview. This was done by content analysis of statements about 36 foreign conflicts by the governments of three “bystander” nations—the United States, Canada, and India—over a 16‐year period. The findings indicate that systemic and situational factors are far more important than domestic factors. States tend to represent wars in congruence with liberalism primarily when their security is already assured by another power or when the conflict does not involve allies, rivals, or fellow democracies. Thus, most of the expectations of realism are supported at the psychological level.

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)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.012
Scholarly communication0.0050.002
Open science0.0000.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.064
GPT teacher head0.442
Teacher spread0.378 · 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 designTheoretical or conceptual
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

Citations73
Published2003
Admission routes1
Has abstractyes

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