Analysis of Economy and Trade among China, India, and Russia under the Belt and Road Initiative
Bibliographic record
Abstract
The Belt and Road Initiative advocated by China is expecting to assist in the infrastructure and financing of participating countries and promote free trade through cooperation with countries along the Belt and Road. China hopes to lead the regional economic integration process through investment-driven trade. Out of geopolitical considerations, Russia and India initially held a relatively negative or cautious attitude towards the Belt and Road Initiative. Therefore, Russia proposed the concept of the Eurasian Economic Union (EEU) in 2011 in order to unite the other independent ASEAN countries based on the customs alliance consisting of Russia, Belarus, and Kazakhstan, and thus create a supranational consortium, which in turn have the ability to compete and cooperate with the Belt and Road Initiative proposed by China. In 2014, India launched the Indian version of the Belt and Road Initiative, named Project Mausam, expecting to promote the integration of economic and trade exchanges around the Indian Ocean with India as the center. However, after recent strikes by the trade war, China actively seeks assistance from India and Russia in order to break through the US trade blockade. During the G20 summit held in Japan in June 2019, China, India, and Russia held a three-party talk. After the talk, the three countries issued a joint statement claiming that “they shall undertake more global responsibilities to protect the fundamental and long-term interests of the three countries themselves and the world”, which seems to have opened up opportunities for future cooperation among the three countries. Therefore, this paper explores the competitive and cooperative relationship among China, India, and Russia under the Belt and Road Initiative.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".