Introduction: Responding to a Changing Arctic Ocean: Canadian and Russian Experiences and Challenges
Bibliographic record
Abstract
Furthering an understanding of Canadian and Russian approaches and challenges in Arctic Ocean governance is the purpose of this thematic issue.A comparison of law and policy perspectives and cooperation between Canada and the Russian Federation has been limited 1 with much more attention being given to great power politics in the Arctic, especially United States-Russian relations.2 A comparison is timely given the fact that Canada and Russia have the longest coastlines in the Arctic and in light of the reality that their Arctic regions are on the front lines of climate change 3 and increasing access to resources and shipping.The articles in this special issue of Arctic Review on Law and Politics are the result of a research project, "Responding to a Changing Arctic Ocean: Canadian and Russian Experiences and Challenges," funded by the Donner Canadian Foundation and co-led by the Marine & Environmental Law Institute, Schulich School of Law, Dalhousie University and the School of Law, Far Eastern Federal University.Seven articles in this main component of the thematic issue address Arctic Ocean boundaries and jurisdiction, security, climate change, Indigenous peoples' rights and interests, marine protected areas and other effective conservation measures, shipping, and fisheries.All articles were written before the Russia-Ukraine crisis.Due to unforeseen circumstances, the oil and gas comparison, "Russian and Canadian
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.028 | 0.007 |
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".