СРАВНИТЕЛЬНЫЙ АНАЛИЗ НАЛОГОВЫХ СИСТЕМ РОССИИ, КАНАДЫ, ШВЕЙЦАРИИ И ЮЖНОЙ КОРЕИ
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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 it