Customs policy as a tool to stimulate exports under martial law
Bibliographic record
Abstract
The purpose of the article was to study the specifics of the functioning of Ukraine’s customs policy under martial law, with a special emphasis on the mechanisms for stimulating export activities and ensuring the efficiency of the state’s foreign economic processes. The methods of analysis, synthesis, comparison, generalisation of data and logical conclusion of the analysis were used for the study. An integrated approach allowed for a systematic study of the multifaceted nature of customs policy, which included interrelated components. The use of the abstract and logical method contributed to the formulation of the key areas of modernisation of Ukraine’s customs policy in the context of martial law and European integration. The paper examined the peculiarities of Ukraine’s customs policy under martial law, analysed the indicators of foreign trade at the beginning of the war and the activities of the State Customs Service. The key objectives of the customs policy were identified: export development, protection of the internal market and support for the competitiveness of Ukrainian goods. For this purpose, customs tariffs with protective and incentive functions were applied in accordance with economic needs and international obligations. Exports declined, particularly in 2022, but stabilised in 2024. The index of physical exports increased, indicating that production processes improved. The agricultural sector suffered the largest losses. At the same time, the EU and Canada’s decision to temporarily exempt Ukrainian goods from duties contributed to trade liberalisation. Military actions and the blockade of ports changed logistics: rail transport replaced sea transportation, which complicated the work of customs due to the increase in the volume of goods flows. It was proposed simplified approaches to export procedures that can stimulate foreign economic activity. The economic effect of the proposed changes was estimated as a basis for new strategic objectives in the field of foreign trade. The practical value of the work is to provide adaptive solutions to the conditions of uncertainty and chaos arising from the martial law in Ukraine, as well as to improve the procedures and processes of foreign economic activity
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".