Genetically modified crops, agricultural sustainability and national opt-outs: Enclosure as the loophole?
Bibliographic record
Abstract
EU Member States face a quandary: after decades of demanding powers to choose whether or not to cultivate GM crops, the EU has returned some limited but significant powers to them. A directive permits Member States to “opt-out” from GM cultivation, provided that they meet relevant criteria. Member States need to decide urgently and carefully whether and how to restrict GM crops, as the permeable nature of the environment facilitates the spread of genetically modified organisms (GMOs) once cultivated. One consideration is agri-sustainability. In principle, GM crops could promote agri-sustainability, including through increasing agrobiodiversity, as they facilitate introducing new traits or species into an ecosystem. However, the nature of their modifications allows for the applicability of patenting law, which enables the legal “enclosure” of the crops’ genetic make-up. This impacts negatively upon the long-term availability of plant genetic resources and agrobiodiversity, as farmers and other breeders operate in a context where accidental cultivation of patented material can still attract liability. This article argues that legal enclosure could justify imposing restrictions on GM cultivation in order to conserve agrobiodiversity as an exhaustible natural resource essential to agri-sustainability. To improve the likelihood of restrictions being upheld legally at both the EU and WTO level, such justifications must be distinguished clearly from any broader environmental concerns, as both the EU and WTO impose stringent restrictions where environmental objectives are raised.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.039 |
| Scholarly communication | 0.015 | 0.025 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.014 | 0.012 |
| 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".