Of Politics and Law: Analysing the Implications of the US-China Trade War on International Law and International Trade Law
Bibliographic record
Abstract
Since taking office in 2017, the president of the United States of America (US), Donald Trump has been on an offensive on the trade front. His administration has levied tariffs on goods coming from China, which retaliated by levying tariffs against the US. This has led to a trade war between these two economies. The economic warring took a turn for the worse with the arrest of Chinese financial executive for Huawei, Meng Wanzhou in Canada on request from the US Department of Justice. She was accused of making false statements to HSBC Bank in 2013 which significantly understated Huawei’s relationship with Skycom. The arrest came after the US levied tariffs on Chinese goods, and also attempted to bar imports of Huawei products. In light of the above, the question that begs for an answer is: Does the US-China trade war undermine the principles of international law and the WTO rules? The article aims to answer the question of the propriety or otherwise of the ongoing US-China trade war within the ambit of international law and the World Trade Organisation economic framework.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.011 | 0.032 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".