The goals of the EU seal products trade regulation: from effectiveness to consequence
Bibliographic record
Abstract
ABSTRACT When policies are adopted, it seems reasonable to assume that they address a certain issue and provide means to mitigate specific problems. This seems the case with the EU's regime on trade in seal products, but it becomes evident that the goal formulation in this case is blurry and unclear. Taking animal welfare, the so-called ‘Inuit exemption’, and internal market harmonisation into account, this article examines the goals of the seal products trade regime and how they are applied. It becomes clear that the attainment of goals bears consequences that are unprecedented due to conceptual and formulation difficulties. Given the indistinct goal formulation during the policy-shaping process and the goal formulation in the policy itself, it seems fair to say that the regime does not aim to improve animal welfare standards in the commercial seal hunt, but rather aims to shut down the commercial hunt completely. This, however, affects Inuit and non-Inuit seal hunters equally and is inconsistent with secondary goals that are formulated in the EU's documents relating to the Arctic. Therefore, the seal products trade regime has consequences that challenge the EU's ambitions in the north.
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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 | 0.026 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| 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".