Topical Questions of Developing the Russian North: Compensation and Incentive Systems Intended to Attract and Consolidate the Population in the Northern and Arctic Regions
Bibliographic record
Abstract
The Object of the Study. The North and the Arctic.The Subject of the Study. Regional premium rates and rated increases.The Purpose of the Study. Studing of the impact of state guarantees and compensation for persons working and living in the Far North and in the equivalent areas, on the involvement and consolidation of the population, including young people. The Main Provisions of the Article. The characteristics of natural and climatic conditions of the Northern regions and their impact on health and life expectancy, as well as methodological approaches to the size of the regional premium rates are presented. On the basis of statistical data territorial differences in the cost of living of the population in the Arctic regions of the Russian Federation and their compliance with the size of regional premium rates are determined. It is proposed to make amendments in the labour legislation about the practice of accrual of rated increases for young people born and bred in the North. While preparing proposals for improving Northern guarantees and compensation it is necessary to take into account the experience of foreign Northern countries (Canada, Sweden, etc.) of attracting and consolidating qualified specialists and workers in the North.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".