Tax System Of The Russian Federation And Foreign Countries
Bibliographic record
Abstract
The organization issue of the existing tax systems of foreign countries is considered in the paper. The introduction provides the most general information about taxes and fees, that is, the functions they perform, their goals, classification according to certain criteria. Further, the tax systems of the most economically developed countries such as the USA, Canada, Great Britain, France, etc. are considered in the article, as well as the main differences between them, and comparison with the tax system of the Russian Federation. The purpose of the scientific paper is to describe the procedure for calculating individual taxes, the method for generating budget revenues, the specification of the most priority areas of tax systems in certain countries, namely, which taxes are applied in priority order in these countries, what their interest rate is, which taxes replenish the state budget the most, and etc. The conclusion that can be drawn: the tax system is one of the most significant economic regulators, and, in view of various factors, its own taxation system has developed in each country. An ideal tax system can exist with some disadvantages. Any country needs to improve its tax system, many of them are approaching this by changing tax laws and making amendments. Effective implementation of the tax system and, accordingly, further economic growth is impossible without the formation of a suitable model of the tax system and the right choice of tax regulation tools.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".