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Record W3080085148 · doi:10.4337/9781839105326.00009

Settlement of disputes

2020· book-chapter· en· W3080085148 on OpenAlexaboutno aff
David A. Gantz

Bibliographic record

VenueEdward Elgar Publishing eBooks · 2020
Typebook-chapter
Languageen
FieldBusiness, Management and Accounting
TopicInternational Arbitration and Investment Law
Canadian institutionsnot available
Fundersnot available
KeywordsInvestor-state dispute settlementDispute resolutionState (computer science)Context (archaeology)Political scienceNegotiationSettlement (finance)International tradeAgency (philosophy)Dispute mechanismFree trade agreementSubsidyLawLaw and economicsBusinessAlternative dispute resolutionEconomicsFree tradeGeographySociologyForeign direct investment

Abstract

fetched live from OpenAlex

The North American Free Trade Agreement (NAFTA) incorporated three distinct dispute settlement mechanisms, addressing (1) investor–state disputes (ISDS) between foreign investors and host states; (2) binational panel review of national administrative agency rulings under domestic anti-dumping (AD) and subsidy/countervailing duty (CVD) laws; and (3) state-to-state disputes challenging another party’s application or interpretation of the agreement. The United States-Mexico-Canada Agreement (USMCA) incorporates the same three mechanisms, two of which, ISDS and state-to-state dispute settlement, are extensively modified. This chapter addresses the individual mechanisms and the context under which the negotiations took place, emphasizing the elimination of ISDS coverage for US-Canada disputes and the limitations placed on such disputes between the United States and Mexico. The possibly significant USMCA improvements to NAFTA’s deeply flawed state-to-state dispute resolution system are also discussed. At the insistence of Mexico, the trade dispute settlement mechanism (Chapter 19) remains largely intact.

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 categoriesMeta-epidemiology (narrow), Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.716
Threshold uncertainty score1.000

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 communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.024
GPT teacher head0.209
Teacher spread0.185 · 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 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
Published2020
Admission routes1
Has abstractyes

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