Dispute Settlement Under the WTO and RTAs: An Uneasy Relationship
Bibliographic record
Abstract
The proliferation of RTAs is a recognized feature of our time. While such agreements are permitted under Article XXIV of the GATT, this has not been without controversy and one aspect which remains unclear concerns the role decisions rendered by RTA dispute settlement bodies play in WTO cases. Are RTA dispute settlement systems in competition with and possibly even in contradiction to the WTO DSU or are they complementary? Can they co-exist or are they cast in eternal opposition? Are they equal or are they inherently subordinate to the WTO DSU? The article considers the WTO’s treatment of RTAs in GATT and WTO case law, and weighs arguments for and against the consideration of RTA decisions by the DSB. The article submits that the DSB should not be blind to the equities of a situation where two states have reached an agreement in an RTA selecting dispute settlement under that body. This is more than a theoretical argument, it has happened, and the increasingly complex co-existence of the WTO with some 400 RTAs suggests that similar problems can arise in the future. Furthermore, these issues deserve a much more open and careful analysis than they have had to date.
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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 | 0.066 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.056 |
| Scholarly communication | 0.024 | 0.032 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.023 | 0.031 |
| Insufficient payload (model declined to judge) | 0.005 | 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".