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Record W4205449602 · doi:10.36742/2410-0919-2021-1-10

WORLD EXPERIENCE OF TAX AND FEES MANAGEMENT

2021· article· en· W4205449602 on OpenAlexaboutno aff
Natalia Ostrovska

Bibliographic record

VenueThe economic discourse · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Issues in Ukraine
Canadian institutionsnot available
Fundersnot available
KeywordsRevenuePopulationEconomicsDeficit spendingDebtTax revenueState (computer science)Public economicsEconomic policyFinanceSociology

Abstract

fetched live from OpenAlex

Introduction. Given the deepening disparities in the economic and social system of the state, political confrontations, resulting in imbalances in public finances and increased debt risks, the tax component should be the basis for the formation of budgetary resources of the state. State revenues determine the social and economic level of development of regions and countries, as well as financial support of the population. Looking at this, the main task of the state is to balance its revenues with expenditures. Since a significant excess of expenditures over revenues lead to an economic crisis, rising public debt, the budget deficit of others. The tax component is the basis for the formation of state budget resources in an unstable fiscal space. This is an important link that contributes to the development of social and economic relations and requires constant monitoring. Methods. The study uses the fundamental provisions of economic theory, tax theory, as well as studies of domestic and foreign scholars. General scientific principles of conducting complex scientific researches were used to solve the set tasks. In the process of research a number of general scientific methods were used, in particular: analogies, logical generalization and system analysis. Results. Based on the selected problems, the foreign experience of such countries as the USA, Germany, Canada, France, Japan, Australia, Great Britain and Italy was studied, on the basis of which the prospects of improving the collection of taxes and fees in Ukraine were singled out. Discussion. In order to solve the problems of collecting taxes and fees, it is necessary to: gradually shift the fiscal burden towards direct taxes, which will be a direct result of increasing the dependence of public authorities on the economic development of the territory; the preservation of the current rather high role of intergovernmental transfers is a consequence of the strong differentiation of regions according to the level of social and economic development and the objective necessity in modern conditions; competition between regions should, first of all, be carried out at the expense of formation of a favourable business environment: improvement of investment climate, development of infrastructure, reduction of administrative barriers, instead of establishment of preferential tax regimes. Prospects for further research on the collection of taxes and fees may be to strengthen the study not only of the level of tax burden, but also the optimal combination of direct and indirect taxes. Keywords: taxes, tax system, budget, budget revenues.

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)0.001
metaresearch head score (Gemma)0.002
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: Other · Consensus signal: Other
Teacher disagreement score0.013
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.002
Scholarly communication0.0030.003
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0130.002

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 head0.023
GPT teacher head0.257
Teacher spread0.234 · 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
GenreOther

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
Published2021
Admission routes1
Has abstractyes

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