Trade Conflict Between the U.S. and China: What Are the Impacts on the Chinese Economy?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Growth in trade has slowed since the global financial crisis in 2008. It seemed to recover in 2017 but declined again after the Trump administration in the U.S. imposed protectionist measures in 2018 which led to conflicts with its major trading partners, including Canada, China, Japan, Mexico, Korea and the EU. Among these partners, the U.S. negotiated amendments to its FTAs with Canada, Mexico and Korea. It is still negotiating with Japan. However, the U.S. government took a different, hard line approach to China in terms of trade based on setting high tariffs on Chinese imports to which China responded by placing high tariffs on U.S. imports. The trade conflict began with criticisms directed at each other, with the U.S. putting its national interest first and China touting a global system of free trade as a key issue. The trade conflict has negatively impacted not only the U.S. and Chinese economies but also the global economy, given that the two economies together as the G2 account for nearly 40% of global output. Therefore, one of the most important challenges for global economic growth is how the conflict might further affect the global economy. This paper analyzes why the trade conflict emerged and how to resolve it. It also focuses on the economic impacts of the trade conflict on the global economy in general, and the Chinese economy in particular. Further, it analyzes how the Chinese government strategically deals with trade negotiations with the United States.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it