Can Trade Restrictions be Justified by Moral Values? Revisiting the Seals Disputes through a Law and Economics Analysis
Bibliographic record
Abstract
The high-profi le World Trade Organization (WTO) dispute on the European Union’s seal import ban has captivated the attention of the international community for over fi ve years. In 2009, Canada requested World Trade Organization (WTO) consultations with the European Union (EU) on the EU seal products ban under the Seal Ban Regulation (SBR) and subsequent amendments, replacements, extensions, implementing measures, and other related measures, 1 and a panel was later established that issued a report in November 2013. 2 This Panel report was appealed, and the WTO’s Appellate Body reached a subsequent decision in June 2014. 3 This chapter intends to revisit the dispute from a legal and economics perspective. Instead of commenting on the merits of the WTO decisions, this * The authors would like to thank Paolo Davide Farah, Bryan Mercurio, David Wilmshurt, and Mu-Hsiang Yu for comments and suggestions on earlier drafts of this chapter. They would also like to thank Haweni Bedada, who provided research assistance. The views expressed by the authors here are personal. This chapter is sponsored by “Standardization and Intellectual Property Management-Key. Universities Research Institute in Humanities and Social Sciences, Zhejiang Province, China.”
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.035 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.007 | 0.009 |
| 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".