Imbalances of Ukraines global economic integration and the de-peripherization strategy in partnership with Canada, the EU, and the USA during post-war reconstruction
Bibliographic record
Abstract
The article provides a comprehensive analysis of the imbalances in Ukraine’s global economic integration and substantiates the need to transform its integration model in the context of post-war reconstruction, shifts in the world’s geoeconomic architecture, and the formation of strategic alliances with Canada, the European Union, and the United States. It identifies the key threats of economic peripheralization, including export-commodity dependence, a low share of industrial exports, loss of positioning within global value chains, and a lack of foreign investment in high-tech sectors. The author proposes the Smart Integration concept as a new model for Ukraine’s de-peripheralization, aimed at comprehensive economic modernization through the synergy of digital transformation, defense cooperation, innovation infrastructure development, institutional economic diplomacy, and the creation of green industrial hubs. The potential of Smart Industry Zones, Industrial Tech Parks, Digital Export Platforms, and mechanisms for establishing international defense technology clusters in cooperation with NATO member states are analyzed. The paper emphasizes Ukraine’s critical role in global supply chain security alliances, including the supply of critical minerals, components for green energy, and the digital industry. It offers practical recommendations for implementing an ESG-based recovery model, fostering a knowledge economy, and attracting foreign capital through public-private partnership instruments. The role of digital platforms and national export infrastructure in enhancing the global competitiveness of Ukrainian businesses is highlighted. The study employs both quantitative and qualitative methods, incorporating statistics from 2022–2025, global integration index assessments, Ukraine’s involvement in international trade agreements, and projections through 2030. The findings provide strategic value for shaping Ukraine’s economic policy in the areas of external economic integration, innovation-led growth, defense-industrial transformation, and the realization of strategic partnership potential with Canada, the EU, and the U.S. within the framework of the new global economy.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 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".