Applying Tax Benefits to Support the Higher Education Sphere: Comparison of Experience of the Countries of OECD and Ukraine
Bibliographic record
Abstract
The article is concerned with researching the role of tax benefits in the financial provision of the higher education sphere. The publication is aimed at analyzing the practice of applying educational tax benefits in Ukraine and world-wide, finding relationships between the amounts of such benefits and the costs for services by educational institutions from private sources. The experience of application of such privileges in Ukraine is provided, in particular the dynamics of granting of educational tax benefits on both VAT and income-tax are analyzed. The problem points connected with estimation of efficiency and stimulating influence of the given instrument in Ukraine are defined. Result of tax benefits is the loss of revenues of the State budget, in world practice they are qualified as tax expenditures and are equated to an alternative form of direct budget expenditure. Based on the analysis of the amount of tax expenditures in the education sphere in the USA, Canada, Korea, Great Britain, Spain and Germany, there is a proven relationship between the provision of educational tax benefits and private spending in the higher education sphere.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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 teacher head, 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".