Bibliographic record
Abstract
Currently, there is an increase in economic and military-political rivalry between the leading states of the world in the Arctic region. This is due to the fact that in connection with global warming and the melting of the Arctic ice, there are opportunities for large-scale extraction of carbon resources, as well as the development of strategically important sea routes. The growing dependence of the world economy on energy resources and, first of all, on oil and natural gas makes the military-political leadership of a number of countries, including Denmark, Canada, China, Norway, Russia, the United States, to resort to active development of new strategies to promote their national interests in zone of the Arctic. To implement this task, all key areas of foreign policy regulation were involved: from scientific research and the peaceful development of the Arctic seas to large-scale military measures in the Arctic Ocean. Thus, the problems of determining the ownership of the energy resources of the Arctic region for the leading northern countries have become a priority, which, in turn, has increased the likelihood of a conflict of interests between them and the emergence of international crisis situations in the Arctic. Considering the above, in recent years, the Russian leadership has been giving priority attention to the development of natural resources in the Arctic. Fundamental strategic documents have been adopted that define the basic principles of Russia's Arctic policy, aimed at realizing its national interests in the region. Thus, given the important geopolitical importance of the natural resources of the Arctic region, it is relevant to study the dependence of the Russian government on natural resources in the Arctic 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.073 | 0.015 |
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".