Pengembangan Potensi UMKM Berbasis Sumber Daya Lokal dalam Mendorong Perekonomian di Desa Margaasih Kecamatan Margaasih Kabupaten Bandung
Bibliographic record
Abstract
Community service research in Margaasih Village, Margaasih Sub‑district, Bandung Regency, aims to map and develop the potential of local resource‑based micro, small, and medium enterprises (MSMEs) through the integration of Geographic Information Systems (GIS) and participatory approaches. The 290.44 ha village is home to 26,083 residents, 44 percent of whom are of productive age (16–59 years). According to village government data, 425 MSME units are spread across five hamlets, predominantly in the trade, culinary, and garment sectors. This study adopts the Resource‑Based View (RBV), Endogenous Growth Theory, and Sustainable Rural Livelihoods Framework (SRLF) to examine the competitive advantages of MSMEs and the dynamics of village development. Employing a Participatory Action Research (PAR) methodology, the research includes situational analysis using GIS, service action planning, implementation of digital management training, and impact evaluation. Business management training, digital literacy workshops, and microfinance facilitation have been proven to enhance the capacity of MSME actors. The resulting “Local Resource‑Based MSME Potential” model is intended as an operational reference for village governments and partner institutions in designing more effective, inclusive, and sustainable economic empowerment programs
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.036 | 0.004 |
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".