The practice of Canada’s use of historically established titles in relation to Arctic Sea spaces
Bibliographic record
Abstract
The basis for this study was the marked similarity of the geographical position of Canada and the Russian Federation as states with a significant continental part in the Arctic and the longest coasts in the Arctic. The interest in the Canadian experience of implementing the Arctic legal policy and, in particular, solving the problem of the legal regime of the Northwest Passage, is caused by the similar problems facing the Russian Federation in relation to the waters of the Arctic straits of the Northern Sea Route. The article deals with the following problematic issues: the role of the sectoral principle in the history of the formation of the status of Canada’s Arctic spaces; the practice of environmental national-legislative regulation of Canada in the Arctic; the concept of historical waters: historical titles in the system of direct baselines in the justification of Canada’s rights to Arctic Sea spaces; Canada’s historically formed position regarding the passage through its Arctic waters foreign courts; experience in the formation of legal titles of the Russian state for the Arctic spaces. The objective of this scientific study is to identify trends in the practice of Canada’s use of historical titles when extending sovereignty and jurisdiction to the adjacent Arctic Sea spaces. The aim is to formulate possible recommendations for improving the normative and law enforcement practice of Russia in this area.
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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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.016 | 0.017 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".