Механизмы реабилитации проблемных регионов в современных условиях: отечественной и зарубежный опыт
Bibliographic record
Abstract
The mechanisms for the rehabilitation of problem areas used by domestic and foreign governmental authorities are considered. The two defining groups of methods in support of problem areas: General (universal), applicable in almost all cases, and personal (custom, private), the use of which is targeted (turn, these methods can be subdivided depending on their level of use at the Federal, regional and local). The concepts of Federal economic and Federal financial aid are refined. Special attention is paid to the retrospective view of the development of state regulation of diverse regions. The most significant examples of rehabilitation of problematic regions of the leading economies of the world such as USA, Canada, Australia and the Netherlands are presented. The conclusion about the uniqueness and diversity of the Russian regional diversity, which has determined the peculiarities of the mechanisms for the rehabilitation of problem areas, which are an integral part in the universal instruments of socio-economic and regional policy. It is identified that a key mechanism for the rehabilitation of distressed regions within such a scenario of socio-economic development must be common for all regions, the process of adjusting their businesses, communities and local authorities to market capitalism Russian-style. It is substantiated that the realities of the development of Russian regions dictate the need to develop the individual program of rehabilitation of the problem areas in the Russian Federation. It is concluded that the necessity of application in Russia the most successful models and specific programs of rehabilitation and support of foreign governments that create their adapted and successful approaches to the resolution of significant regional issues.
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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 | 0.001 | 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 | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".