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Record W2890451784 · doi:10.1142/s0116110505500046

Quota-free Trade in Textiles and Clothing: Policy Issues and Options for ASEAN

2005· article· en· W2890451784 on OpenAlexaboutno aff
Thitapha Wattanapruttipaisan

Bibliographic record

VenueAsian Development Review · 2005
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal trade and economics
Canadian institutionsnot available
Fundersnot available
KeywordsInternational tradeBusinessClothingProcurementTariffFree tradeInternational economicsTrade barrierChinaEconomicsScope (computer science)Rules of originProduction (economics)Market accessSupply chainAgriculture

Abstract

fetched live from OpenAlex

Constrained until 2005 by import quotas, the production and trade of textiles and clothing (T&C) has become widely dispersed with substantial losses in global income and welfare. Meanwhile, quota and tariff preferences from major importing countries have enabled a large number of developing economies to embark on this “starter industry” and to gain a larger market share than would have been the case under an open trade regime. The People’s Republic of China (PRC) and India are expected to benefit most from a quota-free T&C trading environment but at the expense of many economies with preferential treatment, including a large number in the Association of Southeast Asian Nations (ASEAN). There is, however, much scope and several options for the regional economies to mitigate some of the adverse repercussions. Suitable procurement and production mixes of imported inputs, including for up-scale applications in T&C, can yield preferential market access, for example, in the European Union and Canada. An integrated supply chain within ASEAN or linking ASEAN with South and/or East Asia can help global buyers, including those from the United States, to reduce their overdependence on and to offset supply problems from the PRC. Joint ventures with T&C firms in the PRC are an appealing option for the more developed ASEAN economies.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.899
Threshold uncertainty score0.688

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.064
GPT teacher head0.273
Teacher spread0.208 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations4
Published2005
Admission routes1
Has abstractyes

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