Marine Spatial Planning in Canadian Arctic Shipping Governance: Exploring Its Application in the Northern Low-impact Shipping Corridors Initiative
Bibliographic record
Abstract
Abstract This article examines different integrated management frameworks and area-based measures in Canada’s integrated ocean management regime, including their definitions, features and practices. It explicitly analyzes ma-rine spatial planning (MSP) and its practice in Canada’s Atlantic, Pacific and Arctic coasts. Case studies, including the Eastern Scotian Shelf Integrated Management (ESSIM) initiative, Pacific North Coast Integrated Manage-ment Area (PNCIMA) initiative, and Marine Plan Partnership (MaPP), show that MSP has brought additional value to comprehensive ocean planning and facilitated integrated shipping governance. Arctic marine traffic gov-ernance requires an integrated and holistic framework to address the inti-mate relationship and complex interaction between humans and the envi-ronment. The potential application of MSP in developing and governing the Northern Low Impact Shipping Corridors initiative is examined. MSP is con-sidered as a framework or an approach to inform better decision-making for the Corridors initiative from five perspectives: 1) improving interdepart-mental and cross-jurisdictional collaboration; 2) enhancing Inuit engage-ment; 3) encouraging knowledge co-production; 4) supporting data collec-tion and spatial analysis; and 5) facilitating implementation and adaptation of the Corridor initiative.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.007 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.000 | 0.001 |
| 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".