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Record W4293253257 · doi:10.1017/9781009176347.002

Introduction: The Challenge of Global Institutional Change

2022· other· en· W4293253257 on OpenAlexaff
Preet S. Aulakh, Raveendra Chittoor

Bibliographic record

Venuenot available
Typeother
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal trade and economics
Canadian institutionsUniversity of VictoriaYork University
Fundersnot available
KeywordsThrivingClothingInternational tradeTextile industryIndustrial RevolutionEuropean unionBusinessLoomingEconomicsInternational economicsEconomyPolitical scienceLaw

Abstract

fetched live from OpenAlex

What will happen to textile industries in … more than 40 … countries with thriving clothing industries based on exports. They are bracing for the scheduled elimination … of quotas that have governed their exports to the world's two biggest markets: America and the European Union. The quotas have restrained some countries’ exports, but in others, they have created an export industry that might not otherwise have existed. —‘The Looming Revolution: The Textile Industry’, The Economist , 13 November 2004, 76 India has become the world's supplier of cheap … drugs because it has the necessary raw materials and a thriving and sophisticated copycat drug industry made possible by laws that grant patents to the process of making medicines, rather than to the drugs themselves. However, when India signed the World Trade Organization's agreement on intellectual property in 1994, it was required to institute patents on products by Jan. 1, 2005. These rules have little to do with free trade and more to do with the lobbying power of the American and European pharmaceutical industries. —‘Editorial: India's Choice’, New York Times , 18 January 2005, A20 First January 2005 is an important date in the history of the governance of international trade. It is on this day that two global institutional changes took effect that, taken together, altered the trajectory of trade flows in textiles and pharmaceuticals, two industries that play salient roles in different ways in the economies of a large number of developing and developed countries. Negotiated as a part of the General Agreement on Tariffs and Trade’s, or GATT’s, Uruguay Round of talks during the 1986–1994 period, member countries agreed to adopt the Textiles and Clothing (ATC) and Trade Related Intellectual Property Rights (TRIPS) agreements on 1 January 1995, which were to be phased in over the next ten years. The Agreement on Textiles and Clothing abolished the Multi- Fibre Agreement (MFA), which had governed global trade in textiles since 1974. The MFA endorsed bilaterally negotiated agreements on import quotas by developed countries on the exports of textile products from developing countries. Under the new arrangement after 2005, global trade in textiles and clothing would no longer be subject to protectionist quotas but would be governed by the general rules and disciplines embodied in the multilateral trading system.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.038
Threshold uncertainty score0.127

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.008
Scholarly communication0.0100.010
Open science0.0010.003
Research integrity0.0060.007
Insufficient payload (model declined to judge)0.0380.007

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 head0.086
GPT teacher head0.216
Teacher spread0.130 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

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

Citations0
Published2022
Admission routes1
Has abstractyes

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