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Record W4289837695 · doi:10.1093/ejil/chac027

WTO Rulings and the Veil of Anonymity

2022· article· en· W4289837695 on OpenAlexaff
Joost Pauwelyn, Krzysztof Pelc

Bibliographic record

VenueEuropean Journal of International Law · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicWorld Trade Organization Law
Canadian institutionsMcGill University
Fundersnot available
KeywordsAnonymityScrutinyTransparency (behavior)Political scienceOpenness to experienceLaw and economicsDissenting opinionEnforcementLawSociologyPsychology

Abstract

fetched live from OpenAlex

Abstract Despite a general push for greater transparency, opacity continues to play an important function in international tribunals. The World Trade Organization (WTO) is a case in point. While it has done much to increase its openness, the very design of its dispute settlement body is premised on anonymity in some essential respects. We examine two such instances, each dealing with the authorship of dispute rulings. First, we use text analysis tools to demonstrate that the WTO’s panel reports appear to be largely drafted by WTO Secretariat staff rather than the panellists themselves. This appears especially true for the WTO’s most systemically important disputes. Second, we show that the formal anonymity of dissenting opinions, which is required by the WTO’s rules, is a thin veil. Using the most recent Appellate Body’s dissent for demonstration, we use text analysis to pinpoint its likely author. In both these instances, we argue that anonymity exists by design: it serves to strike a balance between judicial autonomy and political control. Yet, in both settings, due to increased scrutiny and widespread access to text analysis tools, the equilibrium relying on anonymity is likely to be upset, with implications for the institution’s future design. We argue that the ultimate result may be a beneficial one and offer a menu of reform options.

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 score0.991
Threshold uncertainty score0.937

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 studies0.0000.000
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 head0.009
GPT teacher head0.238
Teacher spread0.229 · 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

Citations20
Published2022
Admission routes1
Has abstractyes

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