Bibliographic record
Abstract
Due to climate change, the Arctic region becomes a place of geopolitical rivalry of both Arctic and non-Arctic states. Traditional formats for determining the agenda in the region are effective, but with the advent of the interest of an increasing number of international actors, these formats are transforming, which may affect the balance of power in the region. The growing activity of Asian countries in the Arctic, primarily China, is forcing regional states to make adjustments to the development strategy of the region. The rapid renewal of its potential in the northern territories of Russia caused a negative reaction from the western countries, especially after 2014. Such aspirations have emerged as the internationalization of the region by Northern Europe and China, the desire to draw clear boundaries on the part of Russia and Canada, and the buildup of US influence on its colleagues in the North Atlantic bloc. This situation may cause an uncontrolled increase in tension in the region, especially if new alliances between the Arctic and non-Arctic countries are created. The author considers the current approaches of the countries of the Arctic five, analyzes the true motives of internationalization and the role of the format of the Arctic five in maintaining a balance of power and stability in the northern latitudes.
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 | 0.003 | 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 | 0.007 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 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".