World experience of calculation and determination of property taxes
Bibliographic record
Abstract
Əmlak vergisi hal-hazırda dünyanın 120-dən çox ölkəsində tətbiq edilməkdədir. Əmlak vergisinin tətbiq edildiyi ölkələrin əksəriyyətində büdcə gəlirlərində əmlak vergisi üzrə daxilolmaların payı təxminən 1-3% arasında dəyişir. Qeyd etmək lazımdır ki, ABŞ-da həmin göstərici hətta doqquz faizə qədər hesab olunur . Əmlak vergisi üzrə mədaxilin Ümumi Daxili Məhsuldakı dəyəri Kanada, İngiltərə, Amerika Birləşmiş Ştatları, Yaponiya və Fransa kimi inkişaf etmiş ölkələrdə müxtəlif göstəricilərə malikdir. Azərbaycan Respublikasında isə fiziki və hüquqi şəxslərdən tutulan əmlak vergisi mədaxilinin Ümumi Daxil Məhsuldakı dəyəri təxminən 0,3%-a bərabərdir. Azərbaycan Respublikasındakı əmlak vergisinin göstərici səviyyəsi Avstraliya və Avstriya ilə eyni səviyyədədir. Açar sözlər:əmlak vergisi, dünya təcrübəsi, büdcə, torpaq vergisi, ümumi daxili məhsul Laman Amil Amil World experience of calculation and determination of property taxes Abstract Property taxes are currently applied in more than 120 countries around the world. In most countries where property taxes are applied, the share of property tax revenues in budget revenues varies between about 1-3%. It should be noted that in the United States, this figure is as high as nine percent. The value of property tax revenue in GDP varies in developed countries such as Canada, the United Kingdom, the United States, Japan and France. In the Republic of Azerbaijan, the value of property tax income from individuals and legal entities in the Gross Domestic Product is about 0.3%. The indicator level of property tax in the Republic of Azerbaijan is the same as in Australia and Austria. Key words: property tax, world experience, budget, land tax, gross domestic product
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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 | 0.003 |
| 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 | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".