Implications of «One Belt, One Road» Strategy for China and Eurasia
Bibliographic record
Abstract
The «One Belt, One Road» (OBOR) initiative was proposed by Chinese President Xi Jinping during his visits to Kazakhstan and Indonesia, in 2013. The initiative «One Belt, One Road» could be fully treated as a comprehensive domestic and foreign policy concept. OBOR is designed to strengthen China as an attractive actor in the global market and advance its soft power. It is mostly aimed at increasing economic exchanges between China and the world. Historically the concept of the «Silk Road» was not only focused on the trade agenda. It also had rather significant cultural meaning. Obviously, the OBOR initiative could serve as a cultural bridge between China and the world and in this sense, emphases the dialogue between civilizations, not only markets and forces. With its long-term interests, China treats OBOR as a grand strategy. The initiative has been extensively discussed among academics and policy-makers both inside and outside China. The article shows also coordinating efforts of China and Russia in regional development as well as the internationalization of Central Asian region after 1991 and the role of China in this process. Contours of possible great powers rivalry as well as lack of mutual political trust between the Central Asian countries are described. This article intends to provide the analysis on the issue from the Chinese scholars’ perspective.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".