СРАВНИТЕЛЬНЫЙ АНАЛИЗ НАЛОГОВЫХ СИСТЕМ РОССИИ, КАНАДЫ, ШВЕЙЦАРИИ И ЮЖНОЙ КОРЕИ
Bibliographic record
Abstract
В статье рассмотрен опыт взимания налогов в странах с разными экономическими системами (Канада, Швейцария, Южная Корея и Российская Федерация). Проведен сравнительный анализ налоговых систем, состоящих из схожих элементов налогов и принципов налогообложения Выявлены наиболее общие тенденции и различия. Данный анализ может использоваться в дальнейшем для определения направлений совершенствования налоговой системы Российской Федерации. В заключении приведена таблица ставок основных налогов, а также определены существующие недочеты российской налоговой системы.The article examines the experience of tax collection in countries with different economic systems (Canada, Switzerland, South Korea and the Russian Federation). A comparative analysis of tax systems consisting of similar elements of taxes and taxation principles has been carried out. The most common trends and differences have been Identified. This analysis can be used in the future to determine ways to improve the tax system of the Russian Federation. In conclusion, a table of basic tax rates is provided, as well as the existing shortcomings of the Russian tax system are identified.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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".