Bibliographic record
Abstract
The Arctic Region is gaining increasing geopolitical significance as a result of melting glaciers due to factors such as global warming and climate change. The opening of new sea routes and the discovery of rich natural resources have made the Arctic a center of international competition. This vast geography is surrounded by states such as Canada, Denmark (Greenland), Iceland, Norway, Russia, Sweden, Finland, and the USA (Alaska). Among these states, Russia is particularly noteworthy. Russia’s Arctic policy focuses on the utilization of energy and mineral resources, control of new sea routes, and ensuring regional security. Russia considers the rich oil and natural gas reserves in the Barents and Laptev Seas critical for its economy and energy independence. Defining NATO as the primary national security threat in the Arctic, Russia tends to increase its military presence in the region. Through this, it aims to gain a strategic advantage. This study analyzes the geopolitical structure of the Arctic and Russia’s Arctic policy through elements such as energy, military presence, and regional roles. Prepared with qualitative research methods, this study reveals that the strategic and economic importance of the Arctic Region is increasing and that Russia balances the use of energy resources, its military presence, and efforts for international cooperation to protect its interests in the region.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".