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Record W2102517726 · doi:10.5539/ass.v10n19p77

American Political Scientists on the Use of Force: Classifying the Concepts

2014· article· en· W2102517726 on OpenAlexvenueno aff
Darya Sukhovey, Yana Gayvoronskaya

Bibliographic record

VenueAsian Social Science · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Relations and Foreign Policy
Canadian institutionsnot available
Fundersnot available
KeywordsTypologyPoliticsUse of forceEpistemologyIntervention (counseling)Political scienceNational securitySociologyLawPsychology

Abstract

fetched live from OpenAlex

This article argues that existing general typologies of the use of force concepts accepted by American political scientists do not correspond with the reality. The survey compares several distinctive approaches which are generally proposed to classify the ideas elaborated in American political circuits and comes to the conclusion that none of the mentioned approaches could be applied directly to the use of force issue due to numerous difficulties occur while drawing on existing classifications. The article proposes a new method for systematizing these American political theories which is based on two main criteria: scholars’ attitude towards actual use of force and their perceptions of threats/challenges to national security. According to the newly introduced typology there are three major trends in American political thought on the issue: the first one consolidates those who support active and aggressive use of force (proponents of intervention or Interventionalists), the second one includes concepts of those authors who are not against the use of American forces abroad but stand on less aggressive positions (Conceptualists), and the third one unites those American political authors who insist that the US should use the force only in case of direct imminent attack on the American soil (Defenders).

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.950
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0030.010
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.065
GPT teacher head0.397
Teacher spread0.332 · 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; both teacher heads agree on what is shown here.

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

Citations1
Published2014
Admission routes1
Has abstractyes

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