Optimally Enhancement Rural Development Support Using Hybrid Multy Object Optimization (MOO) and Clustering Methodologies: A Case South Sulawesi - Indonesia
Bibliographic record
Abstract
This research aims to propose a strategy for equitable development by gathering information on the living conditions of people in rural areas and grouping villages based on the Community Standard of Living Index (CSLI).Rural areas often face issues such as poverty, inequality, and inadequate access to services, necessitating a rural development strategy for poverty alleviation and empowerment of rural communities.The foundation of successful surveybased research is accurately describing the practices, conditions, experiences, personal characteristics, or opinions of respondents through the questions asked.The stages of this study include the validation of 38 criteria by experts, verification and evaluation using the MOO-Fuzzy Delphi method, weighting with the RR method, village scoring, and clustering using the SOM method.The scores from all respondents were calculated and used as input for the scoring process, which determined the village score.The results indicate that 10 villages fall into the Poor Level of CSLI group.The innovation of this study lies in the method used to develop the Community Standard of Living Index for each village, providing a potential solution for addressing the lack of community participation and delays in presenting information about development conditions in villages.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".