A Frozen Geopolitical Concern: Arctic Ocean and the Emerging Great Power Competition
Bibliographic record
Abstract
The Arctic is on the cusp of a major shift. Defrosting of permafrost, thawing of ice sheet, and degradation of thick sea ice are the results of devastating climate change implications on the region. Arctic ice is melting faster than ever before and therefore, states are rushing to make claims and assert their influence in this region of geo-strategic significance. Climate change has made the Arctic a new geopolitical battlefield. Therefore, the paper poses three major questions: first, what is the strategic significance of the Arctic in contemporary times? Second, what are the domestic and international interests of the US, Russia, and China that are likely to compete in the Arctic? Third, how is the involvement of China- a non-Arctic state- impacting the strategic calculus of the Arctic? The paper argues climate-induced melting of ice caps in the Arctic have encouraged the great powers- US, Russia, and China to view for influence. This influence is derived from the region’s untapped resources and its significance for the maritime trade routes. The paper concludes that even though the Arctic competition can have serious implications on the ongoing great power rivalry, the changing dynamics will also unravel opportunities with regards to maritime trade.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".