Public Morality Exception at the WTO: Much Ado About Nothing?
Bibliographic record
Abstract
Public morality is one of the stated objectives for which WTO Members may seek to justify a measure that impedes trade. The value of considering this single objective is twofold. First, while all six trade disputes relating to the public morality exception have passed the public morality test, no measure has passed both the necessity test and the test in the chapeau paragraph. Second, it is questionable whether it is possible for a panel or the Appelate Body (AB) to recognize a measure as being of public morality. Moreover, if the EC – Seal Products dispute is the focus of the morality exception, it is important to look more closely at subsequent interpretations by panels and the AB, as nothing is less certain about the interpretation of this exception. Two trilogies emerge, firstly the US – Gambling, China – Publications and Audiovisual Products and the EC – Seal Products, and then Colombia – Textiles, Indonesia – Import Licensing and Brazil – Taxation, with the Canada-European dispute as the tipping point. This article is constructed in three parts that draw on the lessons learned from these two trilogies to highlight what remains of the public morality exception at the WTO. public morality exception, WTO law, US – Gambling, China – Publications and Audiovisual Products, EC – Seal Products, Colombia – Textiles, Indonesia – Import Licensing, Brazil – Taxation
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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 | 0.013 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.024 |
| Scholarly communication | 0.016 | 0.018 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.011 | 0.020 |
| 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".