The EU ban on the import of seal products and the WTO regulations: neglected human rights of the Arctic indigenous peoples?
Bibliographic record
Abstract
ABSTRACT The EU ban on the import and commercialising of seals and seal products in the EU market, has attracted intense attention in recent years. As seal products mostly originate from outside the EU, it is argued that the EU action has been discriminatory and hence contrary to the WTO regulations. Canada and Norway have been critical of the EU regulation and have initiated dispute settlement procedures within the WTO since most of the products that enter into the internal market are mainly from these countries. The ban also provoked anger within the Inuit and other indigenous communities, mainly from Canada and Greenland. Although the EU regulation provides an exception for Inuit and indigenous hunts and the subsequent commercialisation of resulting products into the internal market, the exception suffers from clarity and lacks proper implementation procedures. The regulation is predicted to lead to the ultimate disappearance of the seal market in the EU, which directly affects the Inuit and other indigenous peoples engaged in sealing activities. They may lose their means of subsistence. While analysing the critical issues concerning the EU and the WTO regulations and its exceptions, the article focuses on the human rights perspective of the Arctic indigenous peoples affected by the EU ban.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".