The global arms and integrated circuits trade through complex network analysis
Bibliographic record
Abstract
Abstract Open access to a wide range of economic databases enables researchers to explore the global economy using computational methods. This paper applies network analysis to global export data to investigate complex interdependencies. Given that the nature of exported products is a crucial element of foreign policy, the insights gained from such analysis are particularly valuable when viewed alongside concurrent geopolitical trends. This paper examines export data for light arms, ammunition, and related parts, as well as electronic integrated circuits and their components, across more than 220 countries and territories. By creating directed, weighted networks based on export volumes from 2003 to 2022, this analysis identifies key exporting countries in each category and traces shifts in geopolitical influence over time. In the light arms sector, traditional powers such as the USA, France, Germany, Great Britain, and Italy dominate, along with emerging players like Turkey. In contrast, in the field of electronic integrated circuits, East Asian countries-particularly Singapore, Malaysia, Japan, Hong Kong, Taiwan, and China- dominate global trade, surpassing traditionally strong Western economies, such as the USA, Canada, and the European Union.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".