EC – Seal Products: The Tension between Public Morals and International Trade Agreements
Bibliographic record
Abstract
The EC–Seal Products dispute raises fundamental questions about the relationship between public morals and international trade. Can WTO members impose trade restrictions based on moral or ethical concerns? Under what conditions can these concerns trump existing trade liberalization commitments? The dispute was filed in 2009 by Canada and Norway against the EU, which in the same year had banned seal products from being imported and placed on its market. According to the EU, the policy was introduced in response to European moral outrage at the inhumane killing of seals. The EU seal regime included a series of exceptions. In particular, it allowed imports of seal products hunted by Inuit or other indigenous communities, as well as imports of seal products processed and re-exported by EU producers. This article discusses the Appellate Body's ruling in EC–Seal Products and some of the key legal and economic issues raised by this dispute.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".