Self-employed persons as the actors of tax legal relations: problems of normative legal regulation
Bibliographic record
Abstract
This article examines the normative legal regulation of certain provisions of the legal status of self-employed persons who are the payers of self-employment tax in the Russian Federation. Attention is given to the analysis of such characteristics of the legal structure of this special tax regime as taxpayers, object of taxation and tax rate (in the aspect of social security of the actors of these tax legal relations, alongside other categories of self-employed persons). Assessment is given to the content of Russian legislation considering the legal norms of foreign countries (France, Germany, the United States, and Canada) that have positive and long-term experience in the questions of taxation of income of this category of taxpayers. The conclusion is made on the existence of flaws in naming this special tax regime proposed by the legislator, determination of the range of persons who qualify as the taxpayers in such tax legal relations, and the object of taxation. In view of this, the author formulates the original revision of the norms of the Part 1 of the Article 4 and the Paragraph 4 of the Part 2 of the Article 6 of the Federal Law “On Carrying out Experiment on the Establishment of Special Tax Regime – Self-Employment Tax”; as well as advances the opinion on the need to implement the progressive scale of taxation of income of self-employed persons with a mandatory non-taxable minimum and guarantees of their social security on equal terms with other persons engaged in work and professional activity.
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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 | 0.019 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.036 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".