The WTO Appellate Body's Decision-Making Process: A Perfect Model for International Adjudication?
Bibliographic record
Abstract
The functioning of the Appellate Body (AB) is virtually perfect in terms of collegial decision-making. During its first 12 years, it has produced more than 70 reports dealing with controversial trade and non-trade issues with results that are astonishing. First of all, the AB has usually met the strict 90-day deadline to render its decisions, established by the Dispute Settlement Understanding (DSU). Second, despite the fact that the AB has dealt with issues of paramount domestic and international relevance, which could lead to internal divisions among AB Members, the collegial decision-making process of the AB has managed to decide all cases so far with only three separate opinions. And third, even though the AB decides by Divisions, its case law has been coherent through all of them. How has the AB's; collegial decision-making process been able to achieve these results? Former AB Members have provided some descriptions of the AB's; decision-making process. However, they face limitations on the scope of the disclosure of how the AB decides appeals and therefore can provide partial explanations for the success of the institution. This article seeks to fill in this gap using theoretical and comparative analyses to reveal the formal features and informal practices of the AB that allow it to achieve the aforementioned outcomes.
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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 | 0.042 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.037 |
| Scholarly communication | 0.024 | 0.022 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 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".