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Record W2956022590

The WTO’s Under-Capacity To Deal With Global Over-Capacity

2019· article· en· W2956022590 on OpenAlexaboutno aff
Raj Bhala, Nathan Deuckjoo Kim

Bibliographic record

VenueKU ScholarWorks (The University of Kansas) · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicWorld Trade Organization Law
Canadian institutionsnot available
Fundersnot available
KeywordsTransparency (behavior)SubsidyInternational tradeInternational economicsBusinessWorld tradeEuropean unionInternational trade lawCapacity buildingEconomicsPolitical scienceMarket economyEconomic growthLaw
DOInot available

Abstract

fetched live from OpenAlex

As evidenced by World Trade Organization (hereinafter “WTO”) reform proposals, efforts are underway to revise critical features of International Trade Law at the multilateral level. Such efforts are not in a vacuum. Rather, they are occurring in a global economic environment characterized in part by structural imbalances, that is, of over-capacity and consequent overproduction and trade surpluses and deficits. The European Union (EU) and Canada suggest reforms concerning disciplines on state owned enterprises (hereinafter “SOEs”) and subsidies, and enhanced transparency. But, none of their suggestions has received America’s support in particular, nor garnered a significant consensus among WTO Members.
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\nThat failure may be explained by a basic mismatch: proposals fail to address global over-capacity in key manufacturing sectors such as aluminum and steel. Nothing among the reform proposals would alter materially the dearth of disciplines in the General Agreement on Tariffs and Trade (GATT) and WTO on SOEs and SOEs are a core cause of structural imbalances. They also would fail to make key changes with respect to constraining subsidies, bolstering transparency, or enhancing notifications. Accordingly, the thesis of this article is the WTO suffers from an under-capacity to deal with over-capacity.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.769
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.012
GPT teacher head0.215
Teacher spread0.203 · 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.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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

Citations1
Published2019
Admission routes1
Has abstractyes

Explore more

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