The Abuse of “Adverse Facts Available” in US CVD Investigations against China: A Case Study of Two Clean Energy CVD Rulings
Bibliographic record
Abstract
Adverse facts available is a shift of burden of proof adopted by the US Department of Commerce and is liberally used in CVD investigations against China. By the excuse of failure to cooperate to the best of its capacity, positive findings of subsidy could be established in spite of the lack of affirmative evidence and unfavorable substantive rules. In two CVD investigations against China's clean energy products in 2012, adverse facts available was adopted in order to circumvent the substantive rules of public body determinations, electricity and land subsidies and export credit subsidies. It's difficult to get effective remedies under DSB because there's no detailed rule governing the use of AFA, and that WTO dispute settlement decisions only apply at a case-by-case level. Moreover, owing to the passive attitude of US courts, domestic redress in the US also proved fruitless. The solution of this problem, on the one hand, lies in the global cooperation of countries who have come to a mutual understanding about the harms of AFA proliferation, and on the other hand, lies in the clean energy sector itself. The abuse of AFA stems from the escalation of trade conflicts. Consequently, if possible, a solution to this scramble by negotiation is more effective than an outright trade war.
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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 | 0.025 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.012 | 0.010 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.009 | 0.007 |
| 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".