Bibliographic record
Abstract
The article analyzes the most important changes that have taken place over the past five years in the dynamics and geographical structure of foreign trade relations of the European Union (EU) as the largest entity in the system of modern international commodity trade. It is shown that, despite such rather unfavorable external factors as the increase in general turbulence in world commodity trade, the expansion of the zone of protectionist and sanctions measures, trade conflicts, the EU was able to maintain its dominant position in international commodity exchange. At the same time, the European Union has pushed the main competitors, as the latest statistics show a significant decrease in the share of the North American free trade Association (NAFTA, consisting of the United States, Canada, Mexico) and Japan in international exports. Continuing to pursue a balanced foreign trade policy, the EU used its traditional instruments not only to increase its activity in general and outside the European region, but also to maintain a positive balance of foreign trade. In addition, there are hopes for the resumption of goodneighbourly trade relations between the European Union and the Russian Federation, which have been complicated in recent years by the introduction and extension of sanctions measures by the EU and Russia's response. The author presents new statistical materials, numerous calculated data, for the first time introduced into the domestic scientific circulation.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".