MétaCan
Menu
Back to cohort
Record W3153450632 · doi:10.31857/s268667300012650-5

The United States in the Global Arms Market: Analyzing Trends and Assessing Threats to International Security

2020· article· en· W3153450632 on OpenAlexaff
Nikolay Bobkin

Bibliographic record

VenueUSA & Canada Economics – Politics – Culture · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicDefense, Military, and Policy Studies
Canadian institutionsInstitute for Christian Studies
Fundersnot available
KeywordsArms controlInternational tradeArgument (complex analysis)Foreign policyInternational securityNoveltyPolitical scienceBusinessEconomicsPublic administrationLawPoliticsPsychology

Abstract

fetched live from OpenAlex

The purpose of this article is to provide a valid argument in defense of the author's scientific hypothesis that US arms sales have many negative consequences for international security. The analysis is carried out at three main levels. The article begins with an assessment of the main parameters of the global arms trade. It then examines the provisions indicating the growing role of arms sales in U.S. foreign policy, identifies the associated risk factors for stability and security at regional levels. In conclusion, the policy in the field of arms sales pursued under D. Trump is considered. The main attention is paid to the consideration of the decisions of the U.S. President that weaken the control over arms exports, the reasons and nature of the contradictions between his administration and the Congress on arms export issues are analyzed, and threats to international security posed by the U.S. strategy of arms sales are assessed. A definite novelty of the proposed study is a comprehensive analysis of quantitative indicators and a specific strategy of the American leadership in the field of arms sales. The use of the systemic approach has made it possible to view the global arms trade as a relatively holistic and stable set of interrelated elements.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.729
Threshold uncertainty score0.732

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.029
GPT teacher head0.257
Teacher spread0.228 · 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 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
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueUSA & Canada Economics – Politics – CultureSame topicDefense, Military, and Policy StudiesFrench-language works237,207