Bibliographic record
Abstract
This chapter introduces a multidisciplinary collection of chapters addressing various aspects of governance of Arctic shipping written by leading international scholars. It investigates how ocean changes and anthropogenic impacts affect our understanding of risk, policy, management and regulation for safe navigation, environment protection, conflict management between ocean uses, and protection of Indigenous peoples’ interests in Canadian Arctic waters. The book is divided in three parts, together providing a multi-faceted and interdisciplinary view on governance of Arctic shipping. The first part addresses conceptual and empirical aspects of risk governance, management, and assessment in the Canadian Arctic. The second part focuses on the human dimensions of a changing Arctic, providing insights in Inuit perspectives and knowledge, occupational safety issues onboard cruise and other commercial vessels, and aspects of fishing vessel safety. The third part focuses on regulatory considerations of shipping and ocean use, with contributions addressing the IMO’s framework for Arctic shipping, the Polar Code implementation in Canada, and contemporary topics concerning ship emissions, heavy fuel oil, and maritime spatial planning. It is hoped that the contributions encourage further multi- and interdisciplinary work by established and emerging scholars, and that these can assist decision-makers in planning, managing, and regulating Arctic Shipping.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.442 | 0.272 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".