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Record W2610350657

Conflict or cooperation? The successful resolution of U.S. trade disputes.

2003· article· en· W2610350657 on OpenAlexaboutno aff
Jennifer Beth Shulman

Bibliographic record

VenueDeep Blue (University of Michigan) · 2003
Typearticle
Languageen
FieldSocial Sciences
TopicWorld Trade Organization Law
Canadian institutionsnot available
Fundersnot available
KeywordsConflict resolutionResolution (logic)International tradePolitical scienceBusinessComputer scienceLawArtificial intelligence
DOInot available

Abstract

fetched live from OpenAlex

Since the end of WWII, the liberal trade regime has seen an unprecedented rate of expansion and growth. Trade between the U.S.A. and its largest trading partners has been at the forefront of this expansion. With this growth in trade has come changes in the nature of trade disputes. This growth and change, though, has resulted in a system of dispute resolution that varies among the U.S.A.'s trade partners through governmental behavior in the resolution of these disputes. The evolution of trade dispute behavior is evident in the U.S.A.'s use of both domestic and international trade dispute mechanisms, specifically anti-dumping, countervailing duty, Section 301, GATT, and the U.S.-Canada Free Trade Agreement mechanisms. This study examines variance in the U.S.A.'s use of these trade dispute mechanisms with Canada, the European Union, and Japan from 1979--1994 using cross-sectional data and statistical analysis. This study finds that the target country argument, developed herein, explains U.S. trade dispute behavior. The target country argument posits that the use of specific trade dispute mechanisms and the degree of conflict escalation is dependent upon the past relationship with that specific trade partner. Put most simply, the U.S.A.'s use of trade dispute mechanisms varies with the trade dispute partner and the information gathered in each dispute is context specific. Contrary to conventional wisdom, it is found that variance in trade dispute resolution is not explained solely by either interdependence or to domestic interest groups.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.731
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.015
GPT teacher head0.229
Teacher spread0.214 · 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
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

Citations0
Published2003
Admission routes1
Has abstractyes

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