Sino-Russian Frenemies in the Arctic: A Friendship as Fragile as Glaciers
Bibliographic record
Abstract
ABSTRACT: The rapid melting of glaciers which in recent years has changed the Arctic environment, has opened the door to the marketing of products from the north of the world. The possibility of reducing the duration of sea voyages for ships going between Asia and Europe has led many nations to increase investments and therefore their presence in regions including China. In the past decade, China has intensified cooperation with Russia in the Arctic through investments in infrastructure, research, technology, collaborations and international trade agreements. Until now, the Sino-Russian plans in the area were seen as solid and long-term, thanks to mutual interests (economic/military for Russia and commercial for China). However, as this research presents, new factors such as the Ukraine crisis, sanctions and international isolation for Russia, China's willingness to expand trade to the West (especially with Europe and Canada) and its determination to become a key player in the area, as well as mutual suspicions related to national and international interests, show how Sino-Russian cooperation is not as stable as presented. It is under threat from personal clashes between the Kremlin and Beijing that increase distances rather than fosters friendships, further isolating Russia, and leading China to cooperate with new Arctic actors, such as Iceland, Canada and some Scandinavian countries.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".