Prosperity District/City Grouping Using the Smart City-Based Sharia Development Index in Aceh Province-Indonesia
Bibliographic record
Abstract
This study explores the application of smart city-based sharia development indicators across various districts and cities in Aceh Province.The data collection involved FGD outcomes from 20 community members, complemented by secondary data from the Central Bureau of Statistics and related ministries.The collected data were analyzed using cluster analysis.Results demonstrate annual variations in clusters among districts and cities in terms of implementing smart city-based sharia development.Some regions consistently fall into the 'very good' and 'good' clusters, with the majority falling into the middle or 'good' cluster, representing satisfactory welfare levels.Banda Aceh consistently ranks high due to its role as the provincial capital, while several other districts maintain consistent positions within the 'good' cluster.Development disparities are linked to geographical location, social conditions, human resources, natural resources, and the pace of regional development, including governmental structures.The findings of this study can serve as a reference for policy-making related to welfare improvement through the development of shariabased smart cities.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".