Strategi Pengembangan Kecamatan Enrekang Berbasis Indeks Desa Membangun
Bibliographic record
Abstract
The implementation of village development has been regulated by the government throught Law No. 6/2014 on Villages. Article 78 of the Village Law states that village development is an activity that aims to improve the welfare and quality of life of the village community through the fulfillment of basic needs, the development of village facilities and infrastructure, and the development of the local economy through the sustainable use of resources. To determine the target locus for alleviating underdeveloped villages, the government developed the Village Development Index (IDM), which serves as a map for village development. The IDM consists of the Social Resilience Index (IKS); Economic Resilience Index (IKE); and Environmental Resilience Index (IKL), which is a translation of the development needs set out in article 74 paragraph (2) of the Village Law. The Village Development Index is not only useful to determine the development status of each village, which is closely related to its characteristics, but can also be developed as an instrument for targeting village development. The purpose of this study was to determine the classification of village status and to determine the development strategy of village development in Enrekang Sub-district. The benefit of this research is as a reference for the local government in implementing village development programs in Enrekang District. The research methods used in this study are qualitative and quantitative methods with research locations located in 12 villages in Enrekang District, Enrekang Regency. The analysis technique used is Village Development Index Analysis and SWOT Analysis with data collection techniques namely observation, interviews, and questionnaires. The results of this study determine the classification of village status of 12 villages in Enrekang Sub-district and determine the strategy for developing village potential in Enrekang Sub-district.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".