Наукові підходи до класифікації доходів фізичних осіб
Bibliographic record
Abstract
В статті розглянуті підходи щодо класифікації доходів в особистому оподаткуванні, окреслено коло відповідних дискусійних питань. На основі проведеного дослідження запропоновано авторський підхід до класифікації доходів фізичних осіб за критерієм прикладених зусиль. Вибір саме цього критерію ґрунтується на зарубіжному досвіді щодо групування доходів фізичних осіб з метою оподаткування у таких країнах як Іспанія, Фінляндія, Болгарія, Канада, Швеція. \nIn the article approaches to classification of incomes in personal taxation are considered, the circle of corresponding debatable questions is outlined. On the basis of the conducted research the author's approach to classification of incomes of physical persons by criterion of applied efforts is offered. The choice of this criterion is based on the foreign experience of grouping the incomes of individuals for tax purposes in countries such as Spain, Finland, Bulgaria, Canada, Sweden.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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; both teacher heads agree on what is shown here.
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".