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Record W4392145564 · doi:10.1093/ejil/chae003

Consistency Testing in WTO Law and the Special Case of Moral Regulation

2024· article· en· W4392145564 on OpenAlexaboutno aff
Ben Czapnik

Bibliographic record

VenueEuropean Journal of International Law · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicWorld Trade Organization Law
Canadian institutionsnot available
Fundersnot available
KeywordsConsistency (knowledge bases)LawPolitical scienceLaw and economicsSociologyComputer science

Abstract

fetched live from OpenAlex

Abstract There is a debate in World Trade Organization (WTO) law about whether the right to regulate for public interest purposes is conditioned on a requirement to do so consistently. While the early Appellate Body (AB) jurisprudence eschewed consistency testing under the formal legal test, it refrained from explicitly rejecting the practice. Subsequent AB rulings have seemingly adopted a narrow type of consistency testing through the doctrine of ‘legitimate regulatory distinctions’. A case could also be made that WTO tribunals sometimes embrace consistency testing under Article XX of the General Agreement on Tariffs and Trade, although this is not explicitly acknowledged or universally recognized. In Seals, Canada explicitly attacked the European Union’s (EU) seal products ban for its lack of consistency with the EU’s broader animal welfare settings. This dispute provided an opportunity – indeed, an obligation – for the AB to establish a clear doctrine on consistency testing. This article argues that the AB shirked its duty through reasoning techniques that avoided meaningful engagement with the substance of Canada’s argument. The AB did not truly reject consistency testing, but its precise views are hard to glean due to reasoning that is opaque, confused and even contradictory. This article argues that there is a compelling case for consistency testing, at least in certain ‘public morals’ disputes, and that the AB should provide clearer guidance.

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)0.002
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 score0.942
Threshold uncertainty score0.246

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
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 head0.276
Teacher spread0.248 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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