China's interest in the World Trade Organization's deregulation of international textiles trade
Bibliographic record
Abstract
In testimony before the House Ways and Means Committee in February 2000, former US Trade Representative Charlene Barshefsky characterized the WTO deal struck between China and the United States as a ‘one-way’ flow of concessions from China to the United States. Although this argument suited efforts to persuade Congress to grant permanent normal trading relations (PNTR) to China, it is not strictly true. The United States – and the European Union, Canada and Norway – have in fact committed to important changes in their treatment of goods imported from China. In particular, they will eventually be required to eliminate quotas on imports of Chinese textiles and clothing. This concession is not trivial, with important political economy issues at stake in both the United States and the European Union. The concession is also of considerable importance to China. Textiles and clothing make up approximately one-quarter of China's total exports by value, and around one-quarter of China's textiles and clothing exports go to the United States and European Union. This chapter examines China's interest in the deregulation of textiles trade as required by WTO provisions. It focuses on changes in the United States and European Union. As the regulatory barriers in these markets decrease, the benefits to China of its WTO membership will increase.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".