The Legitimacy of International Trade Courts and Tribunals
Bibliographic record
Abstract
Part I. International Trade Courts and Tribunals: 1. Introduction Robert Howse, Geir Ulfstein, Helene Ruiz-Fabri and Michelle Zang; 2. The WTO adjudicating bodies Gabrielle Marcea and Reto Marco Malacrida; 3. The court of justice of the European Union Pieter-Jan Kuijper; 4. The EFTA Court Halvard Haukeland Fredriksen; 5. The United States court of justice Donald C. Pogue; 6. The Federal Courts of Canada Maureen Irish; 7. The case of MERCOSURl Paula Wojcikiewicz Almeida; 8. The Andean Court of Justice Miguel Antonio Villamizar; 9. The case of the economic court of the ISIS Rilka Dragneva; 10. The COMESA Court of Justice James Thuo Gathii; 11. The WAEMA Court of Justice Illy Ousseni; 12. The ASEAN Trade Dispute Settlement Mechanism Michael Ewing-Chow and Ranyta Yusran; Part II. Cross Cutting Studies: 13. A comparative analysis of formal independence Theresa Squatrito; 14. Judicial interaction of international trade courts and tribunals Michelle Zang; 15. Access to trade tribunals - comparative perspectives Ole-Kristian Fauchald; 16. Towards a more just WTO: which justice, whose interpretation? Andreas Follesdal; Conclusions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.012 | 0.039 |
| Scholarly communication | 0.028 | 0.013 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".