Research on the International Law of Economic Sanctions Involved in the Uyghur Forced Labor Prevention Acts of the United States
Bibliographic record
Abstract
The sanctions target of the Uyghur Forced Labor Prevention Act (2019-2020), the Uyghur Forced Labor Prevention Act (2021-2022) and the Strengthening the Uyghur Forced Labor Prevention Act (2023-2024) is to enable China to end the alleged large-scale detention, forced labor and any other serious violations. The economic sanctions imposed by the United States on Xinjiang include unilateral economic sanctions and multilateral economic sanctions jointly with Canada and Mexico, which are comprehensive sanctions against Xinjiang, and are industrial sanctions and targeted sanctions for other countries whose related activities have no connection point with the United States. Based on the theory of universal jurisdiction expansion, the United States proposed that Xinjiang violated its obligations to the entire international community. Any country has the right to take sanctions to urge the Xinjiang government to stop its alleged illegal acts. The theories of obligations to the international community as a whole and responsibility to protect are actually a revision of the original theory of humanitarian intervention. China uses objective descriptive words to restore the truth and break the vocabulary trap of Western values. The political and economic pressure exerted by the United States on China made Xinjiang comply with the controversial international law to change its non-existent behavior, and the most vulnerable groups in Xinjiang suffered the collateral impact of economic sanctions. The economic sanctions imposed by the United States on Xinjiang have eroded the WTO principle and trade liberalization. The Provisions on the Unreliable Entity List, the Rules on Counteracting Unjustified Extra-territorial Application of Foreign Legislation and Other Measures, and the Anti-foreign Sanctions Law of China constitute China’s anti-sanctions legal system. China had announced that it would implement corresponding anti-sanctions measures against institutions and individuals involved in Xinjiang issues.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".