Bibliographic record
Abstract
I analyze a model of residential location choice in which people derive utility from their proximity to open space. When people have such landscape preferences a new residential development contains more people, more tightly packed than is optimal. More surprising, in a model where new residents arrive simultaneously, I find that land price gradients are highly non-monotonic and do correctly reflect the value of open space. On the other hand, when new residents arrive sequentially, land price gradients are nearly monotonic but do not correctly reflect the value of open space. Finally, dynamic equilibria generally have the property that more remote areas are developed before more central areas. These results have a number of interesting implications for policy. In particular; (1) the creation of central city parks is welfare improving, (2) infill development of central city open space is not welfare improving, (3) the ability of regulation to restrict development at the city s limit, greenbelts , to improve welfare does not derive from a taste for nearby open space, and (4), creating small parks in undeveloped areas before they are subject to development pressure may deter leapfrogging development. Finally, the fact that land prices need not reflect the value of open space suggests that hedonic estimates may understate the value of such open space.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".