Foreign approaches to increasing the migration attractiveness of northern, sparsely populated and hard-to-reach territories
Bibliographic record
Abstract
Development of the Arctic zone of Russia and the Far Eastern Federal District and attraction of population to these territories have the strategic importance significance. To achieve this goal, various state programs are being implemented at the federal and regional levels and support measures are being provided. However, despite this, there is a stable migration outfl ow of the population. In this regard, there is a need to study the experience of states with regions similar in their characteristics to the Russian Arctic zone and the Far East. This article aims to identify and summarize foreign approaches to increase the migration attractiveness of northern, sparsely populated and hard-to-reach territories. Based on the use of the comparative method, as well as methods such as analysis, synthesis, induction, deduction, there were selected foreign regions for the study: Alaska, Nunavut, Yukon, the Northwest Territories of Canada, the Federal Northern Territory of Australia, the Norwegian province of Troms-og-Finnmark and the Swedish region of Norbotten. Among the main measures increasing migration attractiveness abroad are used: the use of tax deductions, increased transport accessibility and the implementation of infrastructure projects through public-private partnerships, development of telemedicine, direct payments, educational loans, increased regional wages, tax benefi ts for legal entities that attract citizens from other regions to permanent residence. It is also concluded that the higher the population density, the fewer support measures are applied. The authors conclude that some foreign measures, with a number of exceptions, could be used for the development of Russian regions. At the same time, it is argued that impractical of foreign experience in making direct payments is not successful.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".