Bibliographic record
Abstract
Abstract If jurisdictions are allowed to tax land and capital separately, they tax only land, because capital taxation distorts the allocation of mobile capital. To exploit absentee owners, however, jurisdictions tax land beyond the efficient level. As absentee ownership increases throughout the economy, land taxation results in greater inefficiency. To alleviate the inefficiency of overtaxing land, the higher‐level government intervenes to require jurisdictions to tax both capital and land at a uniform rate, because the desire to attract capital lowers the tax rate. Uniform taxation of land and capital, or property taxation, thus may be more efficient than separate taxation. JEL Classification: H21, H71 Est‐ce que la terre et le capital devraient être taxés d’une manière uniforme? Si on permettait aux diverses juridictions de taxer capital et terre séparément, elles taxeraient seulement la terre parce que la fiscalité imposée au capital crée des distorsions dans l’allocation du capital mobile. Mais, pour exploiter les propriétaires absents, les diverses juridictions imposent un fardeau fiscal sur la terre qui va au delà de ce que l’efficacité commande. A proportion que la propriété absente se développe dans l’économie, la fiscalité imposée à la terre devient source de plus en plus grande inefficacité. Pour réduire cette inefficacité attribuable à la sur‐taxation de la terre, le niveau sénior de gouvernement intervient pour forcer les juridictions juniors à taxer capital et terre au même taux, parce que le désir d’attirer le capital réduit le taux de taxation. Il se peut donc que la taxation uniforme de la terre et du capital, ou de la taxation sur la propriété, soit plus efficace que des taux d’imposition différents.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".