Canada and the Precautionary Principle/Approach in Ocean and Coastal Management: Wading and Wandering in Tricky Currents
Bibliographic record
Abstract
After reviewing the tricky nature of the precautionary principle/approach, such as confusion over terminology and the spectrum of precautionary measures available, the article through a four-part format describes Canadian initiatives and efforts to implement the precautionary principle/ approach in ocean and coastal management. First, Canada's general steps to adopt the precautionary principle are discussed including caselaw developments and the limited embracing of precaution in environmental impact assessment review and strategic planning processes. Second, the paper reviews Canada's efforts to address marine pollution-ocean dumping, land-based, vessel-source and seabed activities- in light of precaution. Third, Canadian experiences with implementing precaution in the field of living marine resource management, including fisheries, aquaculture and biodiversity protection, are summarized. Fourth, Canada's rather non-precautionary responses to the threats of climate change are highlighted. Canada's overall approach to the precautionary principle/approach is characterized in two images-wading and wandering. Canada has taken rather timid steps to implement the precautionary principle and, while strongly embracing precaution in the area of ocean dumping, has largely wandered towards general and weak versions.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.025 | 0.040 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.008 | 0.008 |
| 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".