Scale-Up Collaboration Model of Village-Owned Enterprises for Increasing Village Economy
Bibliographic record
Abstract
BUMDES is a village economic institution that until now has become an important part in realizing village economy independence. In Indonesia nowadays there are almost 50,000 BUMDES with details of 1,000 developed ones, 10,000 developing ones, and 30,000 BUMDES that are still in the pilot stage. One of the efforts that can be done in increasing BUMDES business is BUMDES scale up program. This program can help increase the management of BUMDES in aspects of institutional, innovation, digitalization, and sustainability. BUMDES scale-up must be conducted in collaboration among several parties, including BUMDES, academics, and industry. This research is a formulation of efforts to increase the performance of BUMDES, which aims at developing BUMDES scale-up collaboration model for increasing the village economy. This research uses a qualitative approach using primary data collected using Focus Group Discussion (FGD) technique for BUMDES, academics, and industry. The analysis used are qualitative descriptive and CIPP (Context evaluation, Input evaluation, Process evaluation, and Product evaluation). The results of research have found that BUMDES scale-up collaboration model for increasing the village economy through tripartite cooperation of BUMDES - Academics - Industry has been proven to be able to overcome two problems occurred in BUMDES. Academics are able to help examine the potential and formulate the innovation designs, also provide the training and mentoring. Industry is able to assist in the aspect of technology and market access. The results of research can be a role model in BUMDES scale-up efforts to increase the village economy.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".