Forming a foreign trade partnership strategy in the context of strengthening national economic security: A case study of Ukraine
Bibliographic record
Abstract
Economic security is an important factor in the economic development of a country. Though it relates to all countries, it plays a key role in relation to emerging economies in particular. Given this fact, the purpose of the study is to analyse the main indicators of Ukraine’s development of foreign economic activity and to formulate the country’s foreign trade partnership strategy in the context of strengthening its economic security. Based on the analysis of the country’s potential in the sphere of foreign trade activity, the foreign trade strategic partnership matrix for Ukraine was formed. This helped to identify the countries with asymmetric interaction and relatively low potential of partnerships, as well as the most attractive strategic partner countries. The matrix shows that countries such as Hungary, Italy, China, Great Britain, and the Russian Federation are characterised by relatively lower potential for strengthening partnerships with Ukraine, whereas the USA, France, Canada, Austria, Germany, Poland, Slovakia, the Baltic States, Belarus, Georgia and the Czech Republic are among the priority countries in the context of strengthening foreign trade relations. The results achieved have wide practical implications for politicians and decision-makers.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".