Artificial Intelligence-based Assessment of Regional Economic Cooperation Mechanisms and Benefits under the Belt and Road Strategy
Bibliographic record
Abstract
The proposal of "Belt and Road" has helped these countries along the route to stimulate the development vitality and cooperation potential of their respective domains, which fits well with their common needs and opens a new window of opportunity for their complementary advantages and open development.This paper improves the construction of the new regional cooperation mechanism of the Belt and Road from three aspects: power mechanism, coordination mechanism, benefit distribution mechanism and compensation mechanism.The double difference method is utilized to assess the economic benefits generated under the Belt and Road regional economic cooperation mechanism.The assessment results show that the country with the highest import and export trade dependence of China is New Zealand, which reaches 18.5611, and as the dominant country of the Belt and Road, China's two-way investment in other countries has the highest scale of $124,705.9million, but the index of investment closeness is -1, which indicates that the capital flow between the two sides is mainly a unidirectional investment from China to other countries.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".