Bibliographic record
Abstract
Urbanization is a transformative force reshaping the economies and societies of developing countries today. It has the potential to bring about powerful development dividends, but the rapid expansion of cities in countries where resources are limited is a pressing policy challenge. Guiding the evolving spatial and economic structure, and getting policy right is an urgent and high-stakes task. This book bridges spatial and economic planning, offering practical tools for both analysis and policymaking. It explores seven national and subnational policy instruments – such as National Development Plans, city master plans, and special economic zones – detailing their purpose, formulation, key components, and effectiveness. Additionally, it provides hands-on guidance on a wide variety of analytical tools, such as mapped overlays, location quotients, input–output analysis, multipliers, metrics of inequality and segregation, gravity models, and scenario planning. These tools can help identify spatial trends, assess linkages, and inform policy decisions. With 26 case studies demonstrating real-world applications, this book serves as a resource for policymakers, development practitioners, and academics seeking to learn, develop the field of knowledge, and shape more effective spatial-economic policies.
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 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) | 0.001 | 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".