Air rights development and public assets: an implementation handbook for public entities
Bibliographic record
Abstract
Air rights development (ARD) above/below public assets can achieve a number of smart growth principles while also being a land value capture tool. However, there are several complexities associated with ARD, along with bureaucratic resistance to an unfamiliar form of development. This report will explore the different ways a public entity can address these challenges and build an effective implementation structure for ARD. The research questions will explore the role of the public entity in ARD and the options available to address the associated challenges. For this exploratory research, the methodology will involve an academic literature scan, along with a jurisdictional scan of institutional literature with Boston, New York, Vancouver, and Washington D.C. as primary case studies. The report will be structured around the challenges of ARD, including political, regulatory, facilitation, and economic valuation issues. The report concludes with recommended steps in creating an implementation structure for ARD.
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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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.015 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.027 | 0.018 |
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".