An Ecofeminist Critique of Canadian Environmental Law: The Case Study of Genetically Modified Foods
Bibliographic record
Abstract
The environmental issue of genetically modified (GM) foods is causing considerable controversy in Canada. In late 2007, the Supreme Court of Canada refused to allow organic canola farmers to sue Monsanto and Bayer Cropscience for harm caused by drifting GM seeds, leaving confusion regarding how effectively Canadian environmental law is regulating these risks. The problems with legal regulation of GM foods are symptomatic of the core problem that arises whenever the law tries to address environmental problems. The nature of environmental harms clashes with traditional approaches to law. As feminist legal critics have pointed out, in a legal system primarily premised on liberal individualism and a capitalist economy, the law itself can embody and perpetuate ways of thinking and acting that lead to harm, rather than being a tool for resolving these problems. This is also true of environmental harms.This paper examines how an ecofeminist legal analysis of the law’s treatment of environmental harm can improve the effectiveness of environmental law. Ecofeminist analysis has argued that current approaches to environmentalism embody a patriarchal conceptual framework, including hierarchical thinking, a logic of domination, and normative dualisms such as the separation of humans from nature. A case study of GM foods will be used to tease out the various threads of legal reasoning (such as definitions of harm, regulation of technology and notions of rights and duties) and analyze them through a lens of ecofeminist legal analysis. This case study can be used to illustrate the need for an ecofeminist legal analysis to guide significant reform of many aspects of Canadian environmental law.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.063 | 0.038 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".