Bibliographic record
Abstract
Abstract This chapter presents the EU’s responses with respect to three closely related policies: the approval of genetically modified (GM) crops for sale and (separately) for cultivation and efforts to lift member state bans on EU-approved GM varieties. These most similar cases differ in outcome; with the EU resuming approvals for sale (a change sufficient to placate Argentina and Canada, but not the United States), but not for cultivation and failing to address member state bans despite very permissive decision rules. In these cases, no tariffs were threatened and there was no exporter mobilization. Commission trade officials did push to accelerate approvals. The Commission, which was more favorably disposed toward biotechnology than most of the member states, was able, with the help of very a permissive decision rule, to overcome opposition to approvals for sale, but not for cultivation, reflecting greater concern among regulators about the environmental impacts of GM cultivation than about the safety of GM varieties. The member state governments also balked at forcing their peers to change their policies. There is little evidence that the WTO’s adverse ruling affected any of the protagonists’ preferences.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.051 | 0.022 |
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".