A Review of Canada's Implementation of the Oceans Act since 1997—From Leader to Follower?
Bibliographic record
Abstract
With the passage of the Oceans Act in 1997, Canada was considered a global leader in modern oceans management. With provisions for marine protected areas (MPAs) and integrated oceans management, the Oceans Act was meant to address the existing piecemeal approach to oceans management, by moving to a new approach founded in the concepts of sustainable development and ecosystem-based management. However, the past 13 years of implementation of the Act have highlighted a number of challenges that are affecting the degree to which oceans management is actually changing “in the water.” Most fundamental is the lack of adequate governance mechanisms that will ensure compliance by all parts of the Fisheries and Oceans Canada, and by other federal departments, and enhanced collaboration with all levels of government, including provinces, territories and First Nations. Coupled with inadequate funding, no timelines for completion of plans for MPAs, and the lack of accountability mechanisms, Canada's ocean estate continues to be managed on the piecemeal, sector-by-sector approach that the Oceans Act was meant to replace. More recently, global best practice in MPA establishment is moving to networks and huge MPAs that are fully protected from all human activities, while broader oceans management is moving from an integrated approach to one that includes marine spatial planning and ocean zoning. Canada is just beginning to explore how these new practices could be implemented in its ocean estate, and needs to move more rapidly in order to better conserve and manage ocean ecosystems.
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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 | 0.011 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.026 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".