Bibliographic record
Abstract
There are two different forms of property tax systems: value-based tax, which is used in most countries of the world, and area-based tax, which is used mainly in Central and Eastern Europe and developing countries in Africa. Area-based property tax provides more stable and predictable budget revenues. It is simpler to administer and scores worse on equity grounds from the perspective of the ability-to-pay principle of taxation. Against this background, Israel’s property tax system, known as Arnona, is complex, spatially diversified, and causes a lack of uniformity that leads to tax distortion. This paper’s primary purpose is to identify the weaknesses of Israeli property tax from 1997 to 2017 and indicate how to improve the property tax system. This paper is based on case studies from four of the most important cities in Israel: Tel Aviv, Jerusalem, Haifa, and Beersheba, which have four different measurement methods for calculating property tax. Unique data were collected from the Israel Central Bureau of Statistics. According to this analysis, it was found that there are substantial differences in property tax between the four cities over the two decades analyzed. The main weakness is the lack of uniformity of the taxation system; the solution is to unify the measurement of real estate area for tax purposes using drone technology.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".