Healthy city development: Indonesian government plans to build a new capital city
Bibliographic record
Abstract
The urgency of urban health in Indonesia is very worrying because most of Indonesia’s population now lives in urban areas with minimal supporting infrastructure. That prompted this study to analyze the government’s response to the healthy city development plan in the new capital city. This study uses a qualitative approach that focuses on thematic analysis. It helps check official government documents related to healthy city development plans. The relevant documents that were found were in the form of regulations. This regulation is Law of the Republic of Indonesia Number 3 of 2022 concerning the National Capital (Ibu Kota Negara, IKN). This official document was coded by maximizing the analysis tool, namely NVivo 12 Plus. This study succeeded in mapping several bare references in the healthy city development plan for the new capital city by the Indonesian government. Some of these primary references include the healthy city model (World Health Organization, WHO), the healthy city strategy (Cardiff), and (Vancouver). All of these primary references aim to improve the quality of life of residents in cities through city development that focuses on health. However, there are several challenges that the Indonesian government may face in the future, including problems with air pollution, environmentally friendly transportation, and the provision of green public spaces, health facilities, universal health services, and other infrastructure. This all requires adequate capacity and budget plans, including ensuring transparency in budget management. This study also encourages collaboration between the government, the private sector, and civil society to support the development of healthy cities that run well and sustainably.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".