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Record W3118950307

Inter-budget Relations as a Form of Federalism

2019· article· es· W3118950307 on OpenAlexaboutno aff
Nalezhda Grinchinko, Irina Ignatovskaya, Konstantin Cheprasov, Vera Zubkova

Bibliographic record

VenueRedalyc (Universidad Autónoma del Estado de México) · 2019
Typearticle
Languagees
FieldBusiness, Management and Accounting
TopicCorporate Taxation and Avoidance
Canadian institutionsnot available
Fundersnot available
KeywordsLegislatorRussian federationFederalismSolidarityPolitical scienceFiscal federalismFederal budgetValue (mathematics)Public economicsPublic administrationEconomicsEconomic policyLawDecentralizationPoliticsComputer scienceLegislation
DOInot available

Abstract

fetched live from OpenAlex

We analyzed the existing experience of foreign federal states (USA, Switzerland, Canada, India, and Germany) and establishes that the most interesting for the Russian Federation is the experience of building a federal budget of Germany. In our review we described that in the considered legal orders similar principles and criteria of inter-budget equalization are applied, the choice of which depends on national peculiarities, financial policy, goals pursued by the legislator and specificity of the territorial structure of the country. We noted that the Russian Federation is characterized by an asymmetrical federation, which generates imbalance in the financial provision of the constituent entities. We proposed to carry out budget equalization of the constituent entities of the Russian Federation by means of value added tax and to implement the principles of solidarity and mutual assistance of the constituent entities with horizontal equalization.

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)0.004
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0020.007
Scholarly communication0.0040.004
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.000

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.

Opus teacher head0.013
GPT teacher head0.224
Teacher spread0.211 · 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 designTheoretical or conceptual
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
Published2019
Admission routes1
Has abstractyes

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