Mengoptimalkan Potensi UMKM Lokal melalui Pelatihan Ekonomi Digital di Desa Kotaraja, Lombok Timur
Bibliographic record
Abstract
Digital economy training is an essential step in empowering Micro, Small, and Medium Enterprises (MSMEs) in Indonesia, particularly in rural areas. Kotaraja Village, located in East Lombok, is one such region with high MSME potential and requires innovative approaches to develop and market its local products. Through digital economy training, MSME actors are expected to optimize the use of digital technology to expand their markets, improve operational efficiency, and develop more effective marketing strategies.This training program covers the basics of online marketing, the use of e-commerce platforms, and the utilization of social media as tools to enhance the visibility and competitiveness of local products. Thus, it is hoped that MSMEs in Kotaraja Village will grow, become self-reliant, and compete in broader markets—both locally and globally—through the appropriate use of digital technology.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.018 | 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".