A long way to go till 30 by 30? - An analysis of Canada's legal marine protection
Bibliographic record
Abstract
This literature review examines Canada's current legal ocean conservation measures. I argue that to reclaim its role as global leader in ocean protection and reach the United Nations newly set 30 by 30 goal[1], Canada must improve its marine laws and policies. It should foster cooperation between stakeholders and enhance protection through legal personhood of marine ecosystems. Being the country with the longest coastline in the world, Canada would have a great opportunity to give special weight to the ocean in its legal marine protection. However, after assessing existing legislation and conducting literature reviews on challenges and opportunities for legal ocean protection in Canada, focusing on Marine Protected Areas (MPAs), the research revealed that MPAs that are considered strongly protected make up only 0.4% of Canada's oceans. Reasons for that are adverse interests and the resistance of a strong fishing industry, division of powers issues and problems in the designation and enforcement measures of MPAs. While Canada is doing rather well in passing legislation, it is often left behind other countries with respect to the effectiveness of their implementation. The main reasons for that are the misalignment of policies and missing coordination of responsible actors. Nevertheless, in light of the dire state that our oceans are in, the inability of international law to fix these problems alone and the importance of healthy oceans for humanity, a global leader is urgently needed. [1] https://www.un.org/sustainabledevelopment/blog/2021/07/a-new-global-framework-for-managing-nature-through-2030-1st-detailed-draft-agreement-debuts
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.038 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".