Foreign direct investment along the Belt and Road: A political economy perspective
Bibliographic record
Abstract
Abstract In 2013, China launched its ambitious Belt and Road Initiative (BRI), a large portfolio of infrastructure projects across 71 countries intended to link Eurasian markets by rail and sea. The state-led nature of the Initiative combined with its transformative geopolitical implications have conditioned the type of engagement that many governments and firms in host and third countries are willing to take in Chinese-funded BRI projects. Building on two theoretical streams that have originated in international political economy but have received growing attention in international business, varieties of capitalism and geopolitics, this perspective shows how a greater understanding of the institutional and geopolitical context surrounding BRI helps decipher the selection of host-country firms and third-country MNEs in Chinese-funded BRI projects. We portray firm selection in a BRI project as the outcome of a one-tier bargaining game between China and a host country. We show how institutions and geopolitics influence both the legitimacy gap of Chinese SOEs in a host country and the host country’s relative bargaining power, affecting the likelihood that host firms and third-country MNEs are selected in BRI projects. We also discuss the geopolitical jockeying strategies that these firms can adopt to influence the outcome of the bargaining game.
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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 | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".