Bibliographic record
Abstract
This chapter examines efforts to establish new global rules to restrict export subsidies for coal-fired power plants, which are highly polluting and a major contributor to climate change. Government-backed export credit for coal power plants acts as a form of export subsidy, and thus promotes the expansion of such plants abroad. The US spearheaded multilateral negotiations within the context of the OECD Arrangement to prohibit the use of export credit for coal power plants. However, since China is not part of the Arrangement, it was not a participant in the negotiations or bound by the new disciplines created. China’s absence, I argue, weighed heavily over the negotiations and undermined efforts to construct an ambitious agreement. OECD exporters were extremely resistant to agree to restrict their use of export credit when China—the world’s largest supplier of export credit for overseas coal plants, accounting for nearly half of all export credit in this sector—would face no similar restraints on supporting its exports. Without China’s participation, the impact of the resulting agreement is severely limited. This case thus highlights the difficulty of building effective global trade rules today without the participation of China.
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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 | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| 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".