Farming the Sea, a False Solution to a Real Problem: Critical Reflections on Canada’s Aquaculture Regulations
Bibliographic record
Abstract
Given the dismal state of world fisheries and their continuing decline — exacerbated by climate change — aquaculture is touted by some to be a promising means for fulfilling the growing global demand for seafood, as reflected in its rapid growth as a segment of the global food system. However, large-scale aquaculture presents a complex set of environmental and social issues, and the introduction of genetically engineered fish and seafood add a further layer of complexity to the already contentious nature of conventional aquaculture practices. This article is a critical analysis of aquaculture regulation in Canada. In addition to setting out some of the major issues posed by industrialized aquaculture, it argues that shifting the “production” of seafood from marine fisheries to aquaculture merely shifts the cause of environmental damages. Further, in the context of food security, large-scale aquaculture is an inadequate and oversimplified solution to the problems raised by coastal and Indigenous populations’ reliance on declining fisheries resources. Specifically, using two case studies, this paper criticizes the overreliance within the current system on dominant risk paradigms, which are often closely informed by science. Yet, the relationship between law and science is fraught with tensions, as the two have notably different priorities and methods. In rethinking the role of aquaculture in natural marine resource management, especially in a changing climate, it is important to ensure that careful regard is given to the socio-cultural factors, inequities, and environmental degradation that are inherent in the production of seafood.
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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 | 0.016 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.059 | 0.037 |
| Scholarly communication | 0.022 | 0.006 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.027 | 0.031 |
| Insufficient payload (model declined to judge) | 0.004 | 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".