The Empirical Research on the Key Macroscopical Decision-making Factors for Foreign Countries Implementing Countervailing Policy against China
Bibliographic record
Abstract
At present,with the rapid development of foreign trade,trade friction of China has transformed from the microcosmic aspects of enterprise to the macroscopical aspects of government,and the main target of more than 70% of foreign countries implementing countervailing investigations is China.Based on constructingBN-Lassembled model of key macroscopical decision-making factors,this research draws the relevant conclusions by exerting the related data of United States,Canada,Australia,South Africa,India implementing 38 countervailing files from the year 2004 to 2009 and domestic macroeconomic development,and making an empirical research on the key macroscopical decision-making factors of foreign countries implementing countervailing policy to China.Moreover,the relevant conclusions above obtained are getting further argument and explanation through choosing the file of coated free sheet paper between the USA and China in the year of 2006.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".