Ocean Policy: A Canadian Case Study
Bibliographic record
Abstract
Over the years, Canada, like most other coastal nations, has developed an intricate set of policies and regulatory instruments focused on the management of traditional sectoral uses of the oceans. A decade ago, the necessary steps were taken to modernise the way in which Canadian authorities manage ocean-based activities.\nCanada did not set out to design “one” comprehensive, all inclusive oceans policy. The primary approach taken was to identify, through Canada’s Oceans Act, one federal lead authority responsible for the coordination and harmonisation of existing policy and statutory instruments and to formulate a national vision and guiding principles for oceans management within which existing and emerging policies and laws would be interpreted and implemented.\nThis chapter outlines Canada’s statutory and policy instruments and implementation approach to oceans management. The political and environmental context within which a new management approach was developed will be described as well as the processes which led to the development of the Oceans Act, its policy framework, Canada’s Oceans Strategy and finally, the Government of Canada’s blueprint for action, Canada’s Oceans Action Plan. The relationship between key ocean-related agreements and Canadian domestic law and practice is summarised. In closing, lessons learned during the past decade will be examined, as will the challenges which lie ahead.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.027 | 0.005 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".