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Dispute Resolution under USMCA

2021· article· en· W4290022339 on OpenAlexaboutno aff
Jared Thomas

Bibliographic record

VenueEuropean Journal of Law Reform · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInternational Arbitration and Investment Law
Canadian institutionsnot available
Fundersnot available
KeywordsPolitical science

Abstract

fetched live from OpenAlex

Dispute Resolution under USMCA: Modifications to NAFTA And the Potential Implications In this article the author discusses the differences in the dispute resolution mechanisms between the North American Free Trade Agreement and the United States-Mexico-Canada Agreement. The author notes that while all three dispute resolution mechanisms from NAFTA are present in USMCA, two of the mechanisms have been extensively modified. The author is critical of the Trump Administration’s insistence on eliminating NAFTA’s Chapter 11 provisions on investor-state dispute settlements, leading to a severely weakened ISDS mechanism in USMCA. State-to-state dispute settlement (Chapter 20 under NAFTA) was also significantly modified. The third dispute resolution mechanism, a binational panel process that was covered in Chapter 19 of NAFTA, survived with only minor changes as insisted upon by Canada. As the most extensive changes were made to investor-state dispute settlements and state-to-state dispute settlements, the author analyzes these two dispute resolution mechanisms in detail. The author dissects the changes made to these dispute resolution mechanisms and provides insight into the rationale of the countries involved that led to the changes. The author concludes that with Canada’s refusal to sign on to Annex 14-D and a drastically diluted ISDS mechanism governing ISDs between the United States and Mexico, the most likely outcome under USMCA is that there will be a dramatic increase in Chapter 31 claims when the United States is involved and a heavy reliance on the CPTPP for claims between Mexico and Canada.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.994
Threshold uncertainty score0.552

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 communication0.0000.001
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 head0.028
GPT teacher head0.230
Teacher spread0.201 · 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 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

Citations0
Published2021
Admission routes1
Has abstractyes

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