China-Russian cooperation in the Arctic: A cause for concern for the Western Arctic States?
Bibliographic record
Abstract
Increasing cooperation between China and Russia does not imply the development of an anti-Western pact and a coordinated revisionist strategy pursued by them in the Arctic. The Western Arctic States should monitor China-Russian regional cooperation but refrain from adopting strategies premised on the assumption both are, or will become, deeply aligned with each other. Russia and China should continue to be treated as distinct regional challenges requiring specific strategies towards each. Strengthening strategic solidarity between the Western Arctic States, including the United States (US) as a reinvigorated regional actor, in tackling specific challenges posed by these powers and adapting to a changing Arctic geopolitical environment is necessary. If American regional strategies, however, are solely about confrontation and exclusion against these powers, and pressuring across the board policy conformity onto its allies in these pursuits, these will be insufficient in addressing regional issues and risk marginalizing the autonomy and interests of smaller states like Canada. Facing such possibilities, Canada must continue to develop its own regional capabilities, become more forward leaning in addressing security and economic matters, and increase collaboration with the other smaller Arctic states to avoid the ongoing structuring of the region from becoming entirely dominated by great powers.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".