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Record W2021514631 · doi:10.5539/jsd.v8n4p164

Wars and Military Conflicts of the XXI Century in the Context of the Strategic Interests of the United States

2015· article· en· W2021514631 on OpenAlexvenueno aff
Roman V. Penkovtsev, Natalia A. Shibanova

Bibliographic record

VenueJournal of Sustainable Development · 2015
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Sanctions and International Relations
Canadian institutionsnot available
FundersKazan Federal University
KeywordsVariety (cybernetics)Context (archaeology)Political scienceIdentification (biology)Field (mathematics)PhenomenonConflict resolutionProcess (computing)Cold warInternational relationsSet (abstract data type)EpistemologyLawPoliticsComputer scienceGeography

Abstract

fetched live from OpenAlex

The problem under investigation ??ncerning the phenomenon of war is in the center of attention of scientific thought in the field of international relations. The article is aimed at studying the specifics of wars and military conflicts in the modern world which take place under the indirect or direct participation of the United States. The leading approach to the study of this problem is a systematic approach that allows an integrated use of a variety of scientific methods and techniques. The main results of the study indicate that the complexity of types and structure of modern wars and military conflicts make it difficult to resolve them. However, determining the positions and interests of all participants (direct and indirect) and, above all, the identification of strategic interests of the United States is necessary for the effective resolution, prediction and prevention of most contemporary conflicts in the international arena. The materials of article Submissions may be useful for use in the educational process, as well as in the preparation of educational, methodical and scientific literature, as it contains the theoretical principles and a set of specific proposals to improve the post-Cold War international system.

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)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.512
Threshold uncertainty score0.157

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.047
GPT teacher head0.231
Teacher spread0.184 · 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 teacher head, 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

Citations5
Published2015
Admission routes1
Has abstractyes

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