Integrating Local Culture in Smart City: ‘Sombere’ Based Governance Collaboration in Makassar City, Indonesia
Bibliographic record
Abstract
This research aims to examine the integration of local culture in a smart city. The novelty of this research introduces the concept of a Culturally Integrated Smart City, which emphasizes the importance of integrating the Smart City technology framework with local cultural values so that the development of smart cities becomes more inclusive, sustainable, and rooted in local wisdom. The research used a descriptive qualitative. The main data source in this research was obtained from informants. The initial informants were selected purposefully: people who understand problems in collaborative governance and smart cities. The research found that effective communication through face-to-face dialogue in development planning provides space for the community to convey aspirations, which become part of government policy. In addition, building trust between the government and the community has proven crucial in managing Smart City initiatives, especially in developing technological infrastructure such as CCTV surveillance systems and tourist alley revitalization programs. The commitment of local governments, the private sector, and active community participation in every stage of the implementation of the Sombere-based Smart City in Makassar reflects solid collaboration based on local cultural values. The limitations of this research emphasize the aspects of collaboration and cultural integration in Smart Cities but have not discussed in depth the technical and economic aspects that also affect the sustainability of Smart City.
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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 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".