The United States in the Global Arms Market: Analyzing Trends and Assessing Threats to International Security
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".