Bibliographic record
Abstract
The purpose of this article is to research how companies optimize income tax with the ambition to maintain the achieved sales and profits at the highest possible level. Its purpose is to find out whether companies in Slovakia compensate for higher tax liability by tax loss amortization to reduce their income tax payable. Based on the review of literature from the field of legislation concerning the tax loss amortization by using the descriptive statistics of selected corporate and tax indicators, the companies are monitored in order to capture their behavior in paying income tax. The methods of deduction and synthesis are used in this article. The observed corporate and tax indicators are focusing on the relationship between the tax liability arising from corporate income tax, amortized tax losses, and the amount of tax payable in Slovakia in the period from 2015 to 2018. Tax loss can be considered as a tool for tax optimization, which is used by companies in all countries of the European Union, while the scope of its applicability is often limited by a time horizon. The amortization of tax losses has an impact on the amount of tax levied and the subsequent income tax payable, while the possibility to use this tool of tax optimization is influenced by the changing legislation in the period under review.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".