The Conceptual Principles of Identifying the Tax Object for Income Tax in the Context of Digital Transformation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Using modern technologies such as artificial intelligence, machine learning, blockchain, and cloud platforms increases the accuracy and efficiency of accounting data processing, and therefore the transparency of financial reporting indicators. At the same time, the inefficiency of traditional methods in the conditions of digitalization of management increases the risks of distortion of tax information, reduces the level of control, and causes fiscal losses, which is a serious challenge for the tax administration system. The article aims to develop a holistic concept of forming the tax object for corporate income tax, integrated into the digital environment and adapted to modern economic development challenges. The research methodology combines such methods and techniques as content analysis, a systematic approach, analysis and synthesis, comparison, grouping and classification, detailing and aggregation of financial and tax reporting indicators, construction of diagrams and drawings, and modeling. An integrated model and architecture of the digital concept of forming the object of taxation has been built, which allows for the identification of the effectiveness of various approaches in a virtual environment. To substantiate the practical feasibility of the proposed solutions, elements of expert assessment were used, implemented by generalizing the experience of the European Union, Great Britain, Australia, and Canada. The article presents an innovative concept of integrated income tax administration, which combines key accounting principles with algorithms for automated calculation of tax liabilities. The proposed model allows for the reduction of discrepancies between financial and tax indicators, increases the accuracy of reporting, optimizes tax administration procedures, ensures transparent interaction between state bodies and enterprises, and creates conditions for strategic planning of tax payments. The study results form a holistic approach to determining the object of taxation and administering tax liabilities, which agricultural and industrial enterprises can effectively apply when using digital accounting systems and ERP solutions.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it