Genetically Modified Products and Consumer Concerns under W<scp>to</scp>Law
Bibliographic record
Abstract
Much has been written on risk regulation in World Trade Organization law and on the difficulties WTO Members face if they wish to protect their people from potential risks for which they cannot, as yet, produce scientific evidence.The most prominent case was, of course, the Beef-Hormones case1 in which the United States and Canada successfully challenged EC law that banned the use of certain growth hormones.2This case left the general public with a thoroughly bad image of WTO law and its seeming prevalence of free trade interests over health interests.3Public pressure on the EC legislator not to give in is so great that the EC still prefers to suffer retaliation rather than adjusting EC law to the recommendations of the Appellate Body.4 Certainly, public outcry would be even greater if the WTO Dispute Settlement Body ruled that EC regulation on genetically mohfied organisms (GMOS) was in violation of WTO law.5 Genetically modified (GM) food and seed are products that are subject to consumer health concerns6 but, in general, their danger has not yet been proven by scientific evidence.The issue has given rise to much debate, particularly between the EC and the * Junior Professor for Private Law, in particular European Private Law, University of Bremen, Bremen, This is the modified version of a presentation to the 9th International Consumer Law Conference held inThe author may be contacted at: (crott@uni-
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.017 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.030 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 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".