Why Indonesian MSMEs Should Go International : Economic and Social Benefits
Bibliographic record
Abstract
Purpose: With a proper understanding of these opportunities and challenges, it is hoped that Indonesian MSMEs can be increasingly integrated into the global economy, thus providing great benefits to the Indonesian economy as a whole. The transformation of MSMEs to be more open to the international market is not only an option, but also a necessity to ensure economic and social sustainability and growth. Design/methodology/approach: The research uses mixed methods that combine qualitative and quantitative approaches, and the techniques used are in-depth interviews and focused discussions with the owner of CV Arjuna 99 (UMKM). Findings: The results of the study show that the success of CV Arjuna 99 in penetrating and maintaining the international market lies in continuous product innovation, high quality, and close partnerships with local farmers. By utilizing modern processing technology and continuing to develop variants of chips from natural ingredients, this business has succeeded in attracting interest from foreign markets, including countries such as Canada, Japan, Turkey, and Korea. This success not only has a positive impact on business growth, but also creates social benefits for local communities and employees.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".