Small-scale agricultural product marketing innovation through BUMDes and MSMEs empowerment in coastal areas
Bibliographic record
Abstract
A region’s economic growth depends on the development policies based on the wealth determined from the potential of human, institutional and local resources. Furthermore, tThe development needs to link primary sectors with future processing to increase agricultural products’ added value and marketing competitiveness. This study develops an innovative marketing model in agricultural products for small-scale farmers through village-owned enterprises (BUMDes) and micro, small, and medium enterprises (MSMEs) empowerment in coastal areas. One way of realizing this program is by building agribusiness and agro-industry partnerships that are well-planned and associated with other economic sectors' development. The partnership involves community economic institutions, including BUMDes, credit institutions, farmer entrepreneurs, as well as Micro, Small, and Medium Enterprises. BUMDes is a rural-based business with a legal entity managed by the village government to create added value for the community’s agricultural products. Together with MSMEs, these businesses need to support the agribusiness subsystem's development, including trading in agricultural production facilities and business activities. Furthermore, they need to promote agricultural production, support services, a source of market information for rural communities, the main actors of appropriate technology for agricultural products.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".