The Empirical Study on the Decision Factors of Linkage Investigations between Anti-dumping and Countervailing against China
Bibliographic record
Abstract
It is a new situation that Anti-dumping and Countervailing Investigations against China have been in linkage in recent years.According to the new situation and characteristics of the linkage investigations against China,this research made an empirical research by using the related data of 44 Linkage Investigations cases filed against China initiated by United States,Canada,and Australia from the year 2004 to 2010,based on BC-NB model of decision factors for linkage investigations between anti-dumping and countervailing.It selected the variables and different methods from the launching perspective and targeted perspective respectively to test and verify.The results show that the import and export trade condition,exchange rate conditions,economic growth situation and macroscopical decision-making factors are all the significant decision factors of the Linkage Investigations between Anti-dumping and Countervailing.In accordance with trade practices in China,it makes the following suggestions: China should improve the structure of foreign trade,encourage foreign direct investment,and promote the trade balance effectively;it should enhance its economic strength constantly,and promote the upgrading of the industrial structure;it should reject trade protectionism,improve and perfect foreign trade remedy system.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".