The Development of Taxation of Small Business in Russia in the Conditions of the Digital Economy
Bibliographic record
Abstract
The subject of the study of the proposed topic of the article is the tax relations arising in the process of tax regulation by states of taxation of small business in the digital economy. The relevance of small business development as one of the important categories of taxpayers plays an essential role in the formation of the revenue side of the budget, as well as in the solution of social issues in society. The novelty of scientific research lies in the fact that the problems of investing and taxing small business are based on the prospects of digitalization of the economy. The purpose of writing this research is to address the identified problems of small business taxation in the rapidly changing information and communication technologies, Internet sites, the emergence of new paradigms and business models. The problems of taxation of self-employed population in the country are touched, anddirections for their solution are determined. When writing the article, general scientific methods of financial, economic, comparative analysis, analytical and systematic approach to the object of research and methodological approaches in a number of proposals were used. Problems are identified and investigated in the scientific context, and an in-depth analysis is conducted to ensure the development of small enterprises in Russia in the digital economy.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".