Bibliographic record
Abstract
Control over compliance with tax legislation is mainly entrusted to the governments of countries, which can use various instruments to monitor payment of main budget-forming taxes, including corporate income tax. There are many uncertainties and threats to corporate tax revenue, given recent technological advances and current abilities of international companies to do business around the world.Therefore, it is within countries that there are the greatest opportunities for mobilizing tax resources by increasing the level of compliance with tax legislation and eliminating or reducing the tax gap. Based on the results of the analysis of tax gaps in the income tax, it is possible to develop new relevant directions for improving tax policy and tax administration within the country. The article examines the prerequisites and factors that lead to the emergence of a tax gap, substantiates the importance of control and assessment of tax gaps. The meaning and difference between gross and net tax gap is specified. The main approaches to assessing tax gaps are considered - bottom-up approach and top-down approach. The main approaches to assessing tax gaps are considered - bottom-up approach and top-down approach. The differences, features of application and shortcomings of these methods, necessary conditions and elements for applying the chosen approach and sources of information, such as requests, information about taxpayers, data comparison, data of tax audits, are analyzed. The two main goals of the tax gap assessment were considered, namely, the improvement of tax policy, which is related to the "top-down" method, and the improvement of administration of the corporate income tax. At the same time, the use of the results of the assessment of the tax gap depends on the approach used to calculate the corporate income tax. International experience in assessing the size and structure of the tax gap from the corporate income tax is summarized based on the examples of such countries as Great Britain, Canada and the USA, which conduct tax gap analysis to improve fiscal legislation and reduce the level of the tax gap.
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.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".