The Middle Corridor of Eurasia: Transnational Connectivity and Regional Challenges
Bibliographic record
Abstract
Russia's war in Ukraine has disrupted transcontinental trade routes and affected China's exports to the European Union (EU) in particular.In 2022 and 2023, China was the EU's largest import partner, accounting for 21.3 per cent of total EU imports.Between January 2023 and December 2024, EU imports from China decreased by 4.9 per cent, and EU exports to China decreased by 12.5 per cent (Eurostat, 2025).Even though the overall volume of trade was up 1.4 per cent year-on-year in the first four months of 2025, it is still quite far from realising its full potential.The United States has displaced China as the EU's largest trading partner, and that is not easy to reverse.China-EU shipments through the Eurasian Northern Corridor, which runs through Russia, decreased by 40 per cent in less than a year (Avdaliani, 2023).The Eurasian Middle Corridor, also known as the Trans-Caspian International Transport Route (TITR), is well-positioned to become a key trade link between China and Europe.However, its development depends on overcoming a host of strategic challenges, including both geopolitical and logistical hurdles.The Middle Corridor is multi-modal, as it depends on rail, road, and maritime transport and requires significant investments in sea ports and trans-Caspian ferry capacities.The problems of tariff coordination, infrastructure development and a common vision for the Middle Corridor's growth and utilisation stall international collaboration.Several countries of key importance to the longterm success of the project are at odds with each other, while others compete for the attention of big players.The concrete branches of the Middle Corridor, 544
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".