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Record W3141726493

The Curial Review of Arbitral Awards After Vavilov

2020· article· en· W3141726493 on OpenAlexaboutno aff
Alexandre Kaufman, Benjamin W. Jarvis

Bibliographic record

VenueSSRN Electronic Journal · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInternational Arbitration and Investment Law
Canadian institutionsnot available
Fundersnot available
KeywordsAppealSupreme courtStatutory lawJurisprudenceLawPolitical scienceArbitrationAdministrative lawJudicial reviewStandard of reviewStatuteEnforcementLegislaturePresumptionFederal Arbitration ActCompulsory arbitration
DOInot available

Abstract

fetched live from OpenAlex

In Canada (Minister of Citizenship and Immigration) v Vavilov, the Supreme Court of Canada revised the framework for determining the standard of review applicable to the judicial review of administrative action. The Court’s conclusion that the presumption of reasonableness will be rebutted where the legislature provides for a statutory appeal from an administrative decision marked a significant departure from its existing jurisprudence. A statutory right of appeal now signals legislative intent that appellate standards of review apply to statutory appeals of administrative decisions. Vavilov did not explicitly pertain to arbitration. However, the Supreme Court's recent jurisprudence has definitively linked administrative law standards of review to appeals of arbitral awards. Vavilov thus presents an opportunity to assert that more probing standards of review also apply to appeals of arbitral awards. In this article, however, it is submitted that Vavilov should not be interpreted as such. To do so would be to take an undue step back from the pro-enforcement stance towards arbitration that has been adopted throughout Canada and other common law jurisdictions.

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)0.017
metaresearch head score (Gemma)0.057
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: Empirical · Consensus signal: none
Teacher disagreement score0.495
Threshold uncertainty score0.985

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.057
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0100.008
Scholarly communication0.0110.003
Open science0.0020.003
Research integrity0.0130.011
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 head0.010
GPT teacher head0.225
Teacher spread0.215 · 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
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
Published2020
Admission routes1
Has abstractyes

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