Bibliographic record
Abstract
This chapter analyzes China’s impact on the global governance of export credit. For decades, the OECD Arrangement has been held up as a successful example of liberal trade governance, with its system of disciplines proving highly effective in preventing a destructive, competitive spiral of state subsidization via export credit. I show, however, that the rise of China has profoundly altered the landscape of export credit and disrupted its governance arrangements. China has emerged as the world’s largest provider of export credit, but China has refused to join the Arrangement and it has persistently thwarted efforts to negotiate a new set of international rules. China has little incentive to agree to disciplines on its use of export credit, which plays a central role in its development strategy. Despite considerable US pressure, China has refused to capitulate and subject itself to international disciplines that it views as fundamentally against its interests. China has shown that it has sufficient power to stand up to the US in defending its development interests. Yet the result, I argue, is that China’s rise is undermining the liberal regime for governing export credit by eroding the efficacy of existing disciplines and blocking efforts to construct new ones.
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".