Introduction
Bibliographic record
Abstract
As human populations grow and land and natural resources come under greater pressure, land use planning has been an increasingly important subject of policy discussion at the national level. Governments, communities, and indeed all stakeholders are being forced to recognize the importance of not only rationalizing the use to which land is put, but even more importantly ensuring that land and resources are stewarded ecologically for future generations. Rooted in the inherently logical yet incredibly complex notion of sustainable development, intelligent land use and stewardship policies are being implemented in different regions of the world. The progress, however, is far too slow to bridge the gap between current development patterns and existing resources effectively. For example, urban growth continues unabated, while cities are unable to provide basic levels of sanitation, employment, health, and education for current residents. This book is an attempt to survey the global experience to date in implementing land use policies that move us further along the sustainable development continuum. Its chapters include diagnoses of the challenges of implementing sustainable land use policies that appear in different parts of the world. These chapters reveal that some problems are common to all jurisdictions, while others appear unique to particular regions. The book also includes chapters documenting new and emerging approaches such as reforms to property rights regimes and environmental laws. Other chapters offer comparisons of approaches in different jurisdictions that can present insights that might not be apparent from a single-jurisdiction analysis.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.378 | 0.219 |
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".