Introduction to Smart Cities in Asia: Regulations, Problems, and Development
Bibliographic record
Abstract
Abstract Asia Pacific, which makes up 60% of the world’s population, is emerging as a dynamic region in the world in terms of economic and technological growth. It is no surprise that Asian cities are also recognized as leaders in designing smart cities that harness digital information to improve operational efficiency. This chapter provides an overview of the subjects and jurisdictions that this book will cover, outlining its structure as well as the flow of the discussion. This book aims to provide audiences with an overview of smart cities in Asia from different perspectives. While the topic of Smart Cities in Asia: Regulations, Problems, and Development does not address all concerns and questions about smart cities in Asia, the discussions outline regulatory frameworks of some countries, addresses certain problems, and projects the development of smart cities in the region. The book also establishes a network of scholars and practitioners who are interested in researching smart cities. The editors and authors welcome all comments, suggestions, and initiatives promoting scholarship in this area.
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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 | 0.002 | 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 | 0.001 |
| 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".