Bibliographic record
Abstract
Regional planning aims to envision a better future for a region and proposes policies to achieve a desired vision of regional development. It encompasses various aspects of the human and biophysical environments, covering extensive geographical areas or jurisdictions with identifiable common characteristics. In this regard, this book offers a state-of-the-art examination of how contemporary regional planning challenges are addressed globally. It includes contributions from leading researchers and scholars in the field of regional planning from Canada, the USA, Israel, Indonesia, Turkey, and Norway. Based on empirical research, the book discusses a variety of topics, including how the relocation of Indonesia’s capital city could potentially resolve the country’s longstanding regional inequities and multidimensional conflicts; the intricacies of planning in contested cities and the dual role of urban planning in conflict resolution; how regional planning balances local autonomy and regional objectives; river basin development as a sustainable planning strategy for a port city; and positioning the Arctic landscape regions in the global system of growth and development. The book is insightful, thought-provoking, and easy to understand. It could serve as an essential reference material on contemporary regional planning for students, planners, NGOs, government officials and international institutions interested in regional development planning.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.064 | 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".