Bibliographic record
Abstract
The North of Russia is a high latitude area, which includes the Arctic zone. The northern specificity lies in the features of the naturals resources and the human potential. The ethnic structure of human potential is the livelihoods of indigenous peoples of the North. Khanty, Nenets, Komi, Selkup, Saami, Chukci, Eskimos, Evens, Chuvans, Kereki, Koryaki, Lappi are the representatives of indigenous peoples of the Russian North. The active industrial development, the competition in the use of biological resources, the development of tourism, the climate change and the inadequate institutional regulation of life of indigenous peoples are the factors that adversely affect the socio-economic situation of the indigenous peoples of the Russia. The Canada´s Arctic strategy is of particular interest to Russia. The Canada´s Arctic strategy in relation to indigenous peoples of the North means raising the level of well-being through a comprehensive approach to the protection of environmentally sensitive areas of indigenous peoples life, the rights of the indigenous peoples manage their lands and resources, financial resources. The model of sustainable development of the Arctic territories is the foundation of the Russian Arctic policy. This model should provide a comprehensive approach to solving the problems of socio-economic development of the indigenous peoples of the North. The main components of this approach are social, economic and environmental.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.008 |
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".