Analysis on the economic effect of Sino-US trade friction from the perspective of added value
Bibliographic record
Abstract
Based on the theory of trade added value, this paper discusses the potential actual trade scale and benefit damage degree of the two countries under the background of big country game by measuring the real trade scale of China and the USA, simulating the economic impact of tariffs imposed by China and the USA and utilizing Wang-Wei-Zhu (WWZ) method to decompose the potential changes in Sino-US trade. The results show that: firstly, the size of China-US trade in terms of total value is significantly overestimated and China's overall trade with the USA in 2001-2014 was overestimated by an average of 3.06 percent, of which goods trade was overestimated by 8.06 percent. Secondly, although tariff increases can reduce the degree of trade imbalance between China and the USA to some extent, the adverse effects are mutual and global, and the European Union, the Association of Southeast Asian Nations (ASEAN), Japan and Canada become the main transfer countries of Sino-US trade. Thirdly, the pattern of China's final exports and the US' intermediate exports determines that China's trade interests are more damaged than those of the USA. It is proved that there is a big gap between China and the USA in the depth and breadth of China's participation in the value chain division of labor and the trade scale measured by Gross Domestic Product is more instructive than the total value.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".