Asian city prospects for planning and urban health
Bibliographic record
Abstract
The current rapid, often unplanned urbanisation across Asia has wide-ranging economic, environmental, health, and social impacts. In an attempt to document the implications of this demographic transition, the Journal of Cities & Health in collaboration with the International Society for Urban Health (ISUH) launched a special issue. This special issue is composed of seven original research papers and one commentary that present a fair geographical coverage of urban Asia. This scholarship aims to: 1) enhance the state-of-the-art understanding of health risks, social vulnerability and adaptation policies in cities across Asia; 2) present case studies where local contexts were taken into consideration to respond to local health needs and cultural preferences; 3) highlight new evidence of health risks and the impact of the built environment; and 4) examine the use of emerging digital technologies and big data across diverse sectors for a more sustainable urban living environment. In the current context of COVID-19, new challenges, insights, and opportunities for change have arisen. Specifically, some crowded Asian cities offer successful approaches in battling early outbreaks of COVID-19 and provide a model for keeping the pandemic at bay, even if they can’t completely eliminate infections. Asian cities can make changes to design spatially distanced transport and recreation opportunities and the long-term implications for both infectious and chronic diseases. This editorial challenges urban policymakers to better align city planning processes with societal goals and public values, for sustainability, health and health equity, and to hold the people of the city as the central plank in all planning processes.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.030 | 0.006 |
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".