Hot Stakes in the Arctic: Global Rivalries and New Geopolitical Forces
Bibliographic record
Abstract
The Arctic, so exquisitely remote, seems at times to drift beyond the reach of global politics. In this frigid expanse, the Arctic Council, an intergovernmental forum standing as a rare bridge, kept its most formidable of rivals—Russia and the US—together, compelling the two to cooperate even as they continued to lock horns elsewhere. It seemed almost too good to be true. The enduring East-West peace once held in the Arctic has come under unaccustomed strain due to the Russia-Ukraine conflict, disrupting governance, research and economic activity while challenging decades of practical and operational cooperation across the region’s vast landscapes and seascapes spanning the northern reaches of North America, Europe and Asia. The irony is even more striking as the Arctic States have no direct territorial or sovereignty disputes in the region. Threat perceptions between Russia and the Arctic Seven—Canada, Denmark, Finland, Iceland, Norway, Sweden, and the United States—are now at an all-time high, particularly in the wake of NATO’s recent expansion, casting a long shadow over Mikhail Gorbachev calling the Arctic as a ‘zone of peace’ in his speech in Murmansk 1987. Non-traditional security issues like climate change, sustainable resource development and the concerns of the indigenous community now teeter on the edge of renewed rivalry, military posturing and strategic interests. In examining the intricate geopolitical strands in the Arctic, this Essay looks at how India—as a non-Arctic State, with independent diplomatic ties with both Russia and the NATO-aligned Arctic Seven—positions its interests in the High North. The analysis posits that, despite the Arctic States’ divergent geopolitical priorities, there is a need to restore a degree of lost cooperation. In this context, a renewed faith in ‘Arctic Science’ emerges as a compelling diplomatic tool, a neutral collaborative platform capable of bridging divides, rebuilding trust and carving out a credible pathway for addressing the Arctic’s most pressing challenges.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.014 | 0.021 |
| Scholarly communication | 0.019 | 0.013 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".