Public Policy Management in Determining the Feasibility of the Smart City Project in Malang, Indonesia
Bibliographic record
Abstract
The Internet has increasingly become a critical part in digital era, the Internet has changed how we interact through social media, access information and public services, and navigate spatially.This article argues that adopting a Smart City concept to meet the needs of urban residents should be done in a careful and sustainable manner.Using the city of Malang in the East Java Province as a case study, the research employees a spatial of SWOT analysis, Pentagon Assets Analysis, and 3D IPA.The results showed a disparity of Internet access amongst Malang City residents.The Pentagon Assets results identified that the residents' social and financial capital is still conventionally trying to fulfill their basic needs.Likewise, the spatial SWOT analysis showed that the Internet coverage area was still constrained by the infrastructure network and basic urban infrastructure.This was also supported by the results of the 3D IPA analysis, which showed that most residents have not received good and proper internet network services.The paper offers several public policy implications in the context of the Smart City development.It concluded that policymakers should consider several aspects such as fulfilling basic urban services, increasing community capacity, or fulfilling Smart City development.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".