Demystifying the Role of Chinese Commercial Actors in the Shaping China’s Foreign Assistance: The Case of Post-war Sri Lanka
Bibliographic record
Abstract
Hedging against its potential exclusion from the Trans-Pacific Partnership and other mega trading agreements, China embarked on its 21st Century Maritime Silk Road agenda to generate growth through supply chain integration and infrastructure construction in South Asian economies. However, the characteristics of China’s business-led and elite-oriented overseas development practices created policy gaps in high-risk countries. Based on interviews and fieldwork focused on Chinese State-Owned-Enterprises (SOEs) in Sri Lanka, this paper explores how globalization changed the nature of these SOEs: from policy executor to both policy maker and market actor in host countries and in China. However, these changes in nature and influence of key Chinese economic actors in host countries are actually not reflected by, and are in fact out of step with, the 1994 regulation that lays down the principle of non-interventionism in foreign assistance. Such a mismatch between expansionist business and restrained regulation leads to a new paradigm where businesses, especially SOEs, serve as a bridge between the host country and Beijing to identify areas where business interest and development needs intersect, thereby shaping development financing distribution in a way that facilitates SOEs’ ascension of the global value chain. However, the exclusion of the local private sector from expressing the most pressing needs makes the new paradigm insufficient to integrate the local supply chain. This, in turn, implies the new paradigm is less able to address debt sustainability problems, and geopolitical and ethnic tension in high-risk regions. In order to redress this imbalance, this paper proposes the inclusion of the private sector and civil society into China’s mainly business-led overseas development paradigm.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.012 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".