The quality of Legal Education of Citizens as a Factor of the Tax Security of Ukraine
Bibliographic record
Abstract
The statement of the problem of study is due to the results of the monitoring of the quality of the provision of educational services in the field of tax education to Ukrainian citizens.The relevance of the problem under study are related to accepted recognition that the directions of education development in Ukraine did not have a sufficiently systemic and comprehensive nature, and therefore did not contribute to the formation of an integral state policy in the field of education.The purpose of this article is to highlight some educational problems, as well as provide recommendations for improving the quality of legal education in Ukraine in order to improve its tax security.In order to study the field of legal education in Ukraine, the following scientific methods were applied: dialectical, historical, formal and legal, axiological, hermeneutic.As a result, the following problems have been identified in legal education in Ukraine: absence of unified standards, unnecessary disciplines in curriculum, insufficient practical basis of education, need for highly qualified teaching staff, lack of orientation towards foreign practices, use of old techniques, insufficient number of teachers knowing foreign languages, need in new ways to present information, excessive quantity of law schoolsand estimation problem.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".