Bibliographic record
Abstract
This paper assesses the impact thus far that the termination of trade restrictions under the Multi Fibre Arrangement (MFA) which up to the end of 2004 applied to exports of clothing and textiles in key OECD markets has had on Asian suppliers.The speculation prior to MFA termination had been that large increases of Chinese exports would ensue, and at the expense of other Asian suppliers.Using data from US, EU Chinese and other sources, the picture that emerges is only small impacts on aggregate US and EU imports of clothing and textiles, and equally only small impacts on aggregate Chinese exports of clothing and textiles.There are, however, large changes in the country pattern of trade, and also within more narrowly defined product categories.There are large increases in shipments from China to both the US and the EU, and for the US proportionally more so in textiles than in clothing.But the US accounts for only 20% of China's exports of clothing and textiles, and exports to Japan (comparable in size to the US) hardly change, and to Hong Kong fall sharply.There are also large price falls for shipments to the US and to certain EU countries (Germany).The shares of other Asian suppliers in US markets generally hold up well, with the largest falls occurring in preferentially treated non Asian suppliers such as Mexico.In EU markets, with the exception of India, all non Chinese Asian suppliers experience falls in their market share.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".