Модели межбюджетных отношений федеральной политики регионального развития
Bibliographic record
Abstract
The purpose is to define models of intergovernmental relations for effective federal policy for regional development. The study used methods of logical, comparative and systematic analysis, of scientific generalizations. The analysis of existing typologies of models of intergovernmental relations on the basis of which the grouped features of the studied models (in the degree of independence of the authorities, by interaction of authorities of different levels, on differentiation of subjects of conducting and powers, the distinction between taxes and the equalization of social and economic conditions in various territories). Systematization of the characteristics of models of intergovernmental relations for different countries (American, Canadian, German, Australian, Swedish and British models) is made. Systematization of signs models of intergovernmental relations allows to determine the degree of decentralization of a particular model and further suggests a superstructure in the form of adding other groups of signs and models used in other countries. This setting is due to including the evolution of the budgetary system. Further study of the relation features models of intergovernmental relations and the factors affecting the efficiency of the model chosen, will determine the direction of the current correction in the Russian economy of federal relations.
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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 | 0.004 |
| 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 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".