THE ECONOMIC IMPACT OF GEOPOLITICAL TENSIONS ON GLOBAL TRADE AND SUPPLY NETWORKS
Bibliographic record
Abstract
Geopolitical tensions significantly impact global supply chains, disrupting trade, logistics, and economic stability. This study examines the economic resilience of Kosovo, Belgium, and Azerbaijan, which face diverse geopolitical challenges due to their differing geographical positions, economic structures, and international trade networks. The research aims to identify key factors influencing economic sustainability in the face of geopolitical disruptions and propose strategies for mitigating risks. A comparative analysis was conducted using qualitative and quantitative methods to assess the impact of geopolitical conflicts, sanctions, and trade restrictions on global supply chains. The study integrates statistical trade data, policy analyses, and case studies to evaluate the geopolitical pressures affecting logistics and international commerce. Findings reveal that economic stability and resilience to geopolitical risks depend on a country’s ability to adapt through infrastructure development, trade diversification, and international cooperation. Kosovo struggles with trade limitations due to partial international recognition and regional instability. Belgium faces supply chain disruptions due to Brexit and EU sanctions against Russia but benefits from strong institutional frameworks. Azerbaijan, while strategically positioned as an energy supplier, must navigate regional tensions and shifting global trade alliances. This study provides novel insights by comparing countries with different economic structures and geopolitical vulnerabilities, highlighting diverse adaptation strategies. The results underscore the need for proactive policy measures, investment in infrastructure, and diversification of trade partnerships to strengthen resilience. These findings offer valuable implications for policymakers and businesses seeking to mitigate geopolitical risks and enhance global supply chain stability.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".