About the progressive tax system of labor remuneration in Russia
Bibliographic record
Abstract
© 2014 Canadian Center of Science and Education. All rights reserved. This paper presents the results of research aimed at improvement of the taxation of wages in Russia. It analyses the problem of determining the progressive income tax scale as being a more socially equitable than the current flat rate in Russia with 13% tax rate on income. Proposed tax scale of tax rates exempts the poor citizens from income tax, shifting the tax burden from the poor to the rich. In accordance with the principle of redistribution that leads to a reduction in income inequality in Russia. The most important source of budget revenues are taxes. In Russia, as in most countries, the tax on personal income (referred to as PIT) is one of the main sources of budget revenues. Its share of the budget is directly dependent on the level of economic development. This is one of the most popular taxes in the world payable on personal incomes. PIT is linked to consumption, and it can either stimulate consumption or reduce it. Therefore, the main challenge of income taxation is to achieve optimal balance between economic efficiency and social justice of the tax. In other words, such tax is required, which would provide the maximum equitable redistribution of income with minimal damage to the interests of taxpayers from taxation. Analysis of tax on personal income shows that it, as well as the whole tax system in the Russian Federation is constantly developing.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".