FOCUS The Textile and Clothing Trade Liberalisation Process
Bibliographic record
Abstract
From the early stages of the Industrial Revolution in Great Britain to the East Asian success story of the last forty years, the textile and clothing industry has played, and continues to play, a central role in the economic development process. Clothing exports are often the first step away from a division of labour in which poor countries export natural resources in exchange for industrial products from developed countries. Today, the share of clothing in the total exports of the poorest countries like Bangladesh and Cambodia is growing and accounts for more than 60 % of their total exports. For middle-income countries like Morocco, Tunisia, or Romania, clothing makes up around 30 % of their total exports. For the developed countries, the share is declining very fast and represents less than 3 % of exports from the USA, the EU, or Korea, Taiwan and Japan. The textile and clothing industry has also always been a very competitive industry due to low entry costs and to the intensive utilisation of low-skilled workers. In the last thirty years, the share of the developing countries jumped from 30 % of world trade to more than 60%. This progress was made in spite of an exceptional trade regime called the Multifibre Arrangement (MFA), implemented for the first time in 1974, and renewed up to 1994. This system was based on quotas on imports by the USA, Canada and the European countries. At
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.021 | 0.002 |
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".