Genetically Modified Crops and Nuisance: Exploring the Role of Precaution in Private Law
Bibliographic record
Abstract
This article critically considers calls for the precautionary principle to inform judicial decision making in a private law context in light of the Hoffman litigation, where it is alleged that the potential for genetic contamination from genetically modified (GM) crops causes an unreasonable interference with the rights of organic farmers to use and enjoy their lands, giving rise to an actionable nuisance. Applying the precautionary principle in this context would likely privilege non-GM land uses over GM uses, given the latter's uncertain environmental impacts. Through a comparison of the institutional characteristics and respective roles of public and private regulation, the authors argue that the private law context, which lacks democratic accountability and has a limited ability to address complex scientific issues, is poorly suited to apply the diffuse, policy-based risk allocation considerations raised by the precautionary principle.
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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 | 0.022 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.078 |
| Scholarly communication | 0.020 | 0.022 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.015 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 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".