Comparative Analysis of the Depositor Rights’ Protection Systems in the Russian Federation and the Republic of Belarus
Bibliographic record
Abstract
The research explores new approaches to reforming corporate tax in the Russian Federation in the part of stimulating research and development. Methodologically it relies on the systematisation of ap? proaches existing in international academic community in relation to the assessment of consequences of applying tax incentives for innovations in particular countries (groups of them), and efficiency of using tax privileges and research and development investments in different countries, industries, companies, at various stages of their lifecycles, as well as factors behind it. The researchers analyse the impact of tax privileges for R&D during calculation of corporate tax in various countries with particular emphasis on the USA, Great Britain, Canada, Germany (world leaders in R&D), and Russia by qualitative and qualita? tive methods. In addition, they scrutinise world trends in reforming taxation of profit earned from R&D. As a result of the research, the authors formulate directions of tax incentives to stimulate growth of in? novation in Russia’s socioeconomic conditions; provide recommendations on changing the procedure of recognising research and development expenditures during the calculation of tax base depending on the expenditures’ types and volume, innovative process, industry, type and size of company activities; justify a preferential taxation regime for revenues derived from the use of intellectual property.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".