IMPLEMENTING MARINE PROTECTED AREAS POLICY: LESSONS FROM CANADA AND AUSTRALIA
Bibliographic record
Abstract
Canada's Oceans Act and Australia's Oceans Policy are based on similar principles and have similar objectives.Both recognize the need for improved oceans management, and include strategies for establishing a system of marine protected areas (MPAs) within a broader marine planning and integrated management context.There are indications, however, that the implementation process has been more successful in Australia than in Canada.This study analyzes and compares a range of factors that may influence the ability of Canada's Oceans Act and Australia's Oceans Policy to achieve their MPA policy objectives.Based on interviews of key informants and reviews of policy and literature, this cross-national comparative analysis reveals the challenges and opportunities of the policy context in each setting, and the relative strengths and weaknesses of the different implementation approaches.The report concludes with lessons and implications for Canada and Australia, and recommendations for other states interested in implementing MPA policy.My family -Mom, Dad and Boohas been a strong foundation of support my whole life.More than family, they are my friends and support network.I appreciate their ongoing moral support, encouragement and confidence in me for all of my endeavours, whether they are academic, personal, athletic or adventurous.Thanks also for the support from my Bubbies and Zaidie.I am very appreciative of all of the help and encouragement that came from my supervisors, all the staff and my friends at REM, who were essential to the completion of this project.Murray, you have been an extremely dedicated and patient supervisor.A warm thanks for always giving me your time and attention when I needed it and never making me feel like a nuisance, even during weekend panic attacks.Thanks to Bill and Wolfgang for their attention, advice and valuable feedback on
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.019 | 0.008 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".