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Record W4414905416 · doi:10.54861/27131211_2025_9_178

ЗАРУБЕЖНАЯ ПРАКТИКА БЮДЖЕТНОГО ФЕДЕРАЛИЗМА И ПЕРСПЕКТИВЫ ЕЁ ПРИМЕНЕНИЯ В РОССИИ

2025· article· ru· W4414905416 on OpenAlexaboutno aff
К.С. Малых

Bibliographic record

VenueПрогрессивная экономика · 2025
Typearticle
Languageru
FieldSocial Sciences
TopicLocal Government Finance and Decentralization
Canadian institutionsnot available
Fundersnot available
KeywordsFederalismRussian federationBest practiceGovernment (linguistics)Fiscal federalism

Abstract

fetched live from OpenAlex

Целью статьи является изучение зарубежных практик бюджетного федерализма и анализ перспектив их применения в российских реалиях. Эффективная система межбюджетных необходима для нормального функционирования российской экономики, и для её формирования мы можем обратиться к практикам иных государств, обладающих большим историческим опытом, чем Россия. Проводя комплексный анализ эффективности зарубежных практик и рассматривая перспективы их реализации с учётом специфики российской экономики, автор ставит целью разработать комплекс мер, которые бы позволили сформировать в России эффективные и справедливые механизмов перераспределения финансовых ресурсов между федеральным центром и регионами. Научная новизна исследования заключается в многостороннем рассмотрении практики формирования межбюджетных отношений в различных странах и практическом анализе перспектив её применения в российской экономике с конкретными примерами. При исследовании использовались такие методы, как анализ, синтез. В ходе исследования рассмотрена практика межбюджетных трансфертов в таких странах, как Германия, США, Канада, Индия. На основании этих данных были выдвинуты предложения о реализации в России таких практик, как горизонтальное бюджетное выравнивание между регионами, формирование межбюджетных трансфертов в виде целевых грантов, предоставление преференций труднодоступным регионам. Практическая значимость исследования заключается в возможности совершенствования системы межбюджетных отношений в Российской Федерации за счёт данных мер, что позволит оказать существенную помощь развитию регионов. The purpose of this article is to study international practices of fiscal federalism and analyze the prospects for their application in the Russian context. An effective system of interbudgetary transfers is essential for the proper functioning of the Russian economy, and to develop one, we can look to the practices of other countries with greater historical experience than Russia. By conducting a comprehensive analysis of the effectiveness of international practices and considering the prospects for their implementation, taking into account the specifics of the Russian economy, the author aims to develop a set of measures that would enable the creation of effective and equitable mechanisms for the redistribution of financial resources between the federal center and the regions in Russia. The scientific novelty of this study lies in its comprehensive examination of the practice of forming interbudgetary relations in various countries and a practical analysis of the prospects for its application in the Russian economy, using specific examples. The study utilized methods such as analysis and synthesis. The study examined the practice of interbudgetary transfers in countries such as Germany, the USA, Canada, and India. Based on these data, proposals were put forward for the implementation of such practices in Russia as horizontal budget equalization between regions, the formation of interbudget transfers in the form of targeted grants, and the provision of preferences to hard-to-reach regions. The practical significance of the study lies in the potential for improving the system of interbudgetary relations in the Russian Federation through these measures, which will significantly support regional development.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.028
Threshold uncertainty score0.095

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0040.009
Scholarly communication0.0120.007
Open science0.0010.003
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0280.010

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.

Opus teacher head0.009
GPT teacher head0.306
Teacher spread0.297 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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