Transforming Smart City Governance for Quality of Life and Sustainable Development in Semarang City, Indonesia
Bibliographic record
Abstract
This study seeks to thoroughly understand the catalysts driving Semarang, one of Indonesia's cities, to become a smart city, with the ultimate goal of improving the long-term well-being of its citizens.Driven by the various barriers that exist in urban development, we begin with a thorough examination of the causes influencing this shift, focusing on the critical role played by Semarang's local government.Semarang, with its rich history and environmental challenges, provides a distinct case study of urban life in Indonesia.Our research used a diverse approach to unravel the rich narrative of Semarang's evolution, including interviews and observational analysis.Our findings highlight the function of the local government in crafting concepts of sustainable development, innovation and community engagement into Semarang's urban planning framework to aid its transition to a smart city.Key drivers of this transition include the development of local regulations, government readiness, and stakeholder collaboration.While seemingly small, these efforts have had a tremendous impact on human progress, providing important insights for the community.This study needs to continue with comparisons with other cities, we want to learn about lessons that can be used to optimize urban ecosystems around the world, ensuring that the quality of life for all citizens improves.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".