China’s Approach to the Belt and Road Initiative: Scope, Character and Sustainability
Bibliographic record
Abstract
As a new form of regional multilateralism, the Belt and Road Initiative is China’s most significant strategic move for external engagement in international economic law, following its World Trade Organization accession. This paper analyses China’s approach towards the Belt and Road Initiative from a legal perspective, focusing on three questions: first, what is the proper scope of the Belt and Road Initiative? Second, is there an identifiable approach that China adopts in the Belt and Road Initiative context, and, if so, what is its key legal characteristic? Third, is China’s Belt and Road Initiative approach sustainable? Employing a functional approach in defining the Belt and Road Initiative, the article identifies three qualities to China’s approach to the Belt and Road Initiative: (i) that it is a hub-and-spoke network, (ii) it adopts a three-track institutional and mechanism approach, and (iii) a dual- track normative approach. Compared with the US trade approach (particularly the US–Mexico–Canada Agreement), these qualities reveal the key characteristic underpinning China’s Belt and Road Initiative approach: maximised flexibility regarding institutions and norms to address uncertainties and challenges in the Belt and Road Initiative. Such flexibility will likely assist in ensuring that China’s Belt and Road Initiative approach is sustainable by enabling trial-and-error, if properly managed. However, it also gives rise to concerns around China’s Belt and Road Initiative approach, especially as to its predictability, coherence, and transparency.
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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 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".