Challenges faced by Chinese firms implementing the ‘Belt and Road Initiative’: Evidence from three railway projects
Bibliographic record
Abstract
In 2013, China launched its ‘Belt and Road Initiative’ (BRI) as a major effort to enhance international trade and economic development. An important feature of the BRI is that it supports free trade regimes and a world economy based upon open regional cooperation. The concept of BRI involves establishing a transport route between China and participating countries to provide more profitable trade and investment corridors. There are few comprehensive studies examining the social and environmental impact on development in recipient countries. To address this gap, this study gathered empirical evidence on railroad projects in three key countries: Indonesia, Ethiopia, and Kenya. The comparative analysis revealed that while political leaders signed agreements that welcomed China’s BRI in support of their national transport development plans, the implementation of these ambitious infrastructure projects faced significant management and operational challenges that had not been foreseen by the Chinese partners. More effective implementation of BRI infrastructure projects in the future will require better understandings of governance, specifically through harmonization with the cultural, institutional and political contexts in partner countries. Social and cultural characteristics of the countries where Chinese firms are working need to be well understood if sustainable and inclusive benefits from the BRI infrastructure projects are to be delivered. Further research on the benefits gained by local people living in the areas affected by the BRI investments is needed.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| 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".