Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.031 | 0.040 |
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; both teacher heads agree on what is shown here.
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".