Development trends of trade and economic relations between Ukraine and NAFTA countries
Bibliographic record
Abstract
Methods. The theoretical and methodological basis of the research is the work of domestic and foreign scientists. The results were obtained with the application of a system of methods. In particular, the methods of grouping, classification and quantitative comparisons were used in the study of the current economic condition of the NAFTA countries, the methods of structural and economic-statistical analysis were used to assess the main financial indicators of trade and economic relations between Ukraine and NAFTA countries, the graphic method was used for a visual representation of the obtained research results. Results. The article is devoted to the analysis of the main trends in the development of trade and economic relations between Ukraine and NAFTA countries. An analytical review of the current economic state of the USA, Canada and Mexico was carried out. The main drawbacks of the North American Free Trade Agreement have been revealed. The provisions of the new US-Canada-Mexico Agreement (USMCA), which was created to replace NAFTA, are characterized. The issue of economic cooperation between Ukraine and NAFTA countries was revealed. A detailed comparative analysis of the main statistical indicators regarding the development trends of Ukraine’s economic cooperation with NAFTA countries in the field of trade in goods and services was carried out. Novelty. The prospects for further development of Ukraine’s trade and economic relations with the NAFTA countries were determined. Practical value. The analysis of the dynamics of export-import operations between Ukraine and NAFTA countries makes it possible to predict the development trends of their economic relations in the future.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".