Міжнародна практика оцінювання податкового розриву з персонального прибуткового податку(International practice of tax gap assessment from personal income tax)
Bibliographic record
Abstract
У статті проаналізовано міжнародну практику оцінки податкового розриву з персонального прибуткового податку, зокрема в Австралії, Канаді, Сполученому Королівстві, Сполучених Штатах Америки та Швеції. Визначено загальне декларування доходів і звітування третіх осіб як основні причини щодо малого розміру податкового розриву із трудових доходів у розвинених державах. Запропоновано підхід щодо оцінювання податкового розриву із податку на доходи фізичних осіб в Україні, який ґрунтується на використанні даних щодо доходів домогосподарств та окреслено перспективність проведення досліджень податкового розриву на постійній основі. (The article deals with the international practice of personal income tax gap assessment, in particular in Australia, Canada, the United Kingdom, the United States of America and Sweden. The compulsory individual incomes declaration and third party reporting are identifi ed as the main reasons for the small size of the employment income tax gap in developed countries. The approach to assessing the personal income tax gap in Ukraine, based on the use of household income data, is outlined as well as the prospects of conducting tax gap research on a regular basis.)
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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 0.007 |
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".