China Strategic Roles Over Artic Region After U.S- Russia Diplomatic Drift in 2015 Crimean Crisis
Bibliographic record
Abstract
Arctic regions, on the northern pole of the earth, have attracted many countries with various interests. The United States, Russia and several Scandinavian countries, each have claims over its regions, which are known to have abundance natural resource. Besides its potential, Artic also become the most vulnerable area whom embrace the direct impact of global warming. Russia as a country with wide territorial borders directly to Arctic region, certainly has a big role in this region. But, Rusia’s relationship with several Western countries such as the US and Canada had not been in a good condition in 2015, since the Crimean crisis. Several psywar through mass media, even led Russian President Vladimir Putin decides to leave the G20 Annual Summit. Putin’s gesture somehow was seen as Russia implied response to the world related to the Crimea crisis which boldly stated that Russia was not afraid of the threat. The instability of Russia's relations with its neighboring countries which incidentally also borders the Arctic region opens the role of other countries to participate in this region, including China. This articles focused on describing out the China Strategic Policy in the Arctic region by identifying its national interests, and what policies China has taken to achieve its goals. The concept of securitization developed by Barry Buzan and Ole Waever is used as an analytical tool to identify China's strategic role in the Arctic region Keywords: China, securitization, national interest , Artic Region
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".