THE ROLE OF SMART CITY POLICY IN IMPROVING THE QUALITY OF LIFE IN SERANG CITY BANTEN
Bibliographic record
Abstract
This research examines the implementation of smart city policy in Serang City, Banten Province, Indonesia, in the context of local government reform from a centralised to a decentralised system. The main objective is to analyse how smart city policy can improve peoples welfare through integrating information technology in e-government, e-budgeting, e-planning, environmental management, and urban planning. The methodology used is a descriptive analysis design with a qualitative approach involving direct data collection in the field and observation of social interactions. The results show that the effectiveness of smart city policy implementation in Serang City is influenced by five main dimensions: environmental conditions, inter-organizational relationships, available resources, characteristics of implementing agencies, and the quality of public services. These factors interact with each other to support or hinder the achievement of policy objectives. This study concludes that the success of smart city policies depends on technology and the ability to manage resources, inter-organizational coordination, and human resource quality development. Continuous evaluation and adjustment of policies and practices are needed to create sustainable and inclusive solutions to improve peoples quality of life. KEYWORDS: Decentralization, Smart City, Serang City, Policy Implementation
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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 | 0.023 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".