Bibliographic record
Abstract
Property taxes are regarded by economists as good taxes for local governments. Yet property tax revenues rarely exceed 3 percent of gross domestic product in any country and usually amount to much less than that. Moreover, jurisdictions that levy property taxes rarely do so in a way that adheres to economic principles. Political pressure to maintain the tax burden at or near its current level for the sake of stability or to favour one group of taxpayers over another often overrides the economics principles of efficiency and equity. The author asserts that no matter how good the property tax is in theory, we need to consider the real-world constraints on the tax. The author sets out what an ideal property tax would look like, contrasting that ideal with what exists around the world, then describes the unique characteristics of the tax that help to explain the gap between theory and practice. Some promising strategies for reform are proposed, along with cautions regarding problematic approaches. Policy makers are well advised to link property tax reform to broader reforms in public sector management aimed at improving public services and governance. Public support for the reform is also very important. Successful implementation requires sustained political will, technical capacity, systems and procedures, financial resources, and time, but it is well worth the effort.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".