Recuperación de plusvalías para el desarrollo urbano: una comparación inter-americana
Bibliographic record
Abstract
Local governments design a broad range of fiscal or regulatory policies that have been inspired by the idea that land value increment may be mobilized to the benefit of the community -that is of land value capture. This paper compares the experiences of North America (US and Canada) and Latin America with value capture tools and discusses the reasons why different policies have been favored and different results and degrees of success obtained in their implementation. Focusing on broad categories of value capture policies, the first part of the paper compares the overall performance and/or experience of the two regions with the capturing of land value increment through conventional taxes, fees and regulatory urban policy instruments. The second part of the paper shows that the same "value capture principle" to address similar problems (to deepen land value taxation; to finance urban infrastructure; to control land use) result in different outcomes (sometimes even opposite) in different contexts, most notably those presented in North America and Latin America, respectively. The paper's concluding section provides some evaluative comments regarding the apparent lag between the intentions and outcomes of value capture policies as experienced by the two regions.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".