The Creation of the Multilateral Trade Court: Design and Experiential Learning
Bibliographic record
Abstract
Abstract The creation of the World Trade Organization (WTO)'s dispute settlement system (DSS) in 1995 remains one of the most puzzling outcomes in international politics and international law in the 1990s. We provide a new explanation for this move to law. We argue that important contextual variables of the negotiations have been largely overlooked by existing explanations, namely ‘experiential learning’. While negotiations to create institutions are characterized by uncertainty about distributional effects, negotiators will look for clues that moderate uncertainty. In the context of the Uruguay Round negotiations, a significant amount of information was drawn from actual practice and experience with the existing General Agreement on Tariffs and Trade (GATT) dispute settlement system. In short, experience gained with judicial institutions and outcomes is important to understand the key results of the negotiations: a legalization leap, more specifically a judicialization of the existing dispute settlement system. We focus on the two dominant actors in the negotiations (the United States and the (then) European Community) and provide evidence for our argument based on an analysis of GATT cases in the 1980s, GATT documents, and in-depth interviews with negotiators who participated in the negotiations.
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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 | 0.028 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".