Legally Containing the Uncontainable: Establishing a Liability Scheme for GE contamination in Canadian Agriculture
Bibliographic record
Abstract
An increasing amount of litigation has been seen to address the spread of genetically engineered (GE) genes; however the focus has largely been on patent infringement to protect the seed developers. Farmers that lose profits due to the contamination of their fields by the (unintentional) flow of gene drift however are often overlooked. This paper tries to address this gap by asking how the current Canadian legal framework deals with the matter of recourse for GE contamination. Finding this system deficient, the paper then looks toward the common law procedures to mediate a solution. An overview of how other jurisdictions have dealt with the matter gives a basis of what opportunities may be available in the Canadian system. I use a socio-ecological framework as well as a more traditional policy analysis to assess the effectiveness of the Canadian regulations in coping with the issue of liability due to contamination. The paper concludes by recommending managing contamination through a compensatory fund on a strict liability basis at the provincial level. The funding ought to come from a seed tax paid by those who benefit financially from the introduction of the GE seeds so as to ensure that both the polluter‟s pay principle is respected as well as allowing for a type of ecological monitoring of the ecosystem.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.018 | 0.006 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".