Resources for sustainable development of Russian Arctic territories of raw orientation
Bibliographic record
Abstract
Attention is drawn to the fate of Russian Arctic regions of raw material specialization, distinguished by single-industry structure of the economy. The urgency of the problem is explained by the inevitable depletion of hydrocarbon and ore resources in the future, as a result of which these regions are threatened with economic depression. The latter may come earlier - due to sharp jumps in world prices for raw materials and the "demarche" of mining companies. The authors believe that to ensure the sustainable development of the Arctic regions of Russia today there are no "iron" recipes. The experience of the USA, Canada and other foreign countries is not always representative. Numerous factors should be taken into account – not only economic, but also ethnic, geopolitical, the factor of "delayed benefit" (in connection (in connection with the planned operation of the Northern sea route), etc. In any case, the restructuring of the regional economy is necessary within the significant centers, implying the emergence of new branches of specialization within the existing resource base, the development of high-tech production, expansion of services (including tourism), transport, computer science, communications, etc. As a specific landfill is considered Yamal-Nenets Autonomous Okrug, according to the authors, the most clearly reflects the nature of the problem. Recommendations on the transition of the Yamal-Nenets Autonomous Okrug from a narrow specialization to a balanced economy, on the transformation of this region into an Outpost of the Russian Arctic are presented.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".