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Record W2947478620 · doi:10.1093/isq/sqz025

Words Matter: How WTO Rulings Handle Controversy

2019· article· en· W2947478620 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Studies Quarterly · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicWorld Trade Organization Law
Canadian institutionsMcGill University
Fundersnot available
KeywordsLegitimacyBalance (ability)PoliticsCompliance (psychology)InstitutionAffect (linguistics)Law and economicsPolitical scienceLawEconomicsSociologySocial psychologyPsychology

Abstract

fetched live from OpenAlex

Abstract The rulings of internationals courts are often reduced to “who won?,” but much more is at stake. Like other institutions, the World Trade Organization (WTO) offers rulings that balance legal discipline against political constraints. We argue that one way in which the WTO handles politically sensitive issues is by increasing the amount of affect in their rulings. In doing so, judges provide national governments with discursive resources to persuade their domestic audiences of the legitimacy of compliance. To test our expectations, we conduct a text analysis of all rulings rendered by the institution since 1995. Specifically, we find that more politically charged decisions, such as the ones concerning nonfiscal rather than fiscal aspects of national treatment claims, are explained in qualitatively different terms. We also find that, as an issue gets ruled on repeatedly, the amount of affect deployed progressively decreases. In sum, the WTO chooses its words strategically to persuade litigants, and their domestic audiences, of the legitimacy of compliance in politically fraught disputes.

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.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.770
Threshold uncertainty score0.999

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.002

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 head0.013
GPT teacher head0.298
Teacher spread0.285 · 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