Bibliographic record
Abstract
Urbanization occurs in tandem with development. Countries in Southeast Asia need to build - individually and collectively - the capacity of their cities and towns to promote economic growth and development, to make urban development more sustainable, to mitigate and adapt to climate change, and to ensure that all groups in society share in the development. This book is a result of a series of regional discussions by experts and practitioners involved in the urban development and planning of their countries. It highlights urbanization issues that have implications for regional - including ASEAN - cooperation, and provides practical recommendations for policymakers. It is a first step towards assisting governments in the region to take advantage of existing collaborative partnerships to address the urban transformation that Southeast Asia is experiencing today. "Urbanization in Southeast Asia: Issues and Impacts is a landmark study on the increasingly urbanized condition of Southeast Asia. It is important because it presents a powerful argument for the role of regional action in developing policy and practical responses to the challenges of urbanization. Thus it offers important lessons for other parts of the world. This study, written by expert authors from within the region, outlines the challenges of urban sustainability, liveability and economic growth that Southeast Asia faces in the 21st century. Thus it provides a valuable roadmap for all concerned with the future of urbanization in Southeast Asia.” - Professor Terry McGee, Professor Emeritus, University of British Columbia, Vancouver, B.C., Canada
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".