Leveraging Land Resource for ULB Finance: Framework and Action Plan for Land Disposal
Bibliographic record
Abstract
Indian cities are coming under pressure to provide infrastructure services to an increasing urban population. With rising population, the value of land (both public and private) is also on rise. Local service delivery and improvement in infrastructure needs sufficient local resources. One option that is being received much attention in municipal finance is land assets (Mohanty 2003, Vaidya 2008). Land is most valuable asset of ULBs and urbanization is driving up its price over a period of time. The investments made by ULBs on their land towards public amenities are also capitalized in the land value. Hence ULBs have the potential to capture the outcome of economic growth by disposing municipal lands for revenue generation. In this paper, an attempt is made to list the important areas which need consideration to develop a frame work for the disposal of municipal land to finance infrastructure requirements for sustainable development.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".