Identifying the Challenges of the Sarawak Malay Terubok Ikan Masin (Salted Fish) Entrepreneur: Qualitative Study
Bibliographic record
Abstract
Sarawak Malay entrepreneur of terubuk ikan masin has become one of the successful entrepreneurs in the entrepreneurial field. Even though the number of the entrepreneurs are still small, but the Sarawak Malay entrepreneur of terubuk ikan masin become one of the main contributor to the state tourism activities. Hence, those entrepreneurs still have to face a lot of challenges and obstacles in the business. Thus, this research aims are to identify the challenges faced and the framework with regards to the challenges faced by the Sarawak Malay entrepreneur of terubuk ikan masin. Using qualitative study to understand the challenges that they are faced in line with this business and finding shows the challenges they faced both personally and in business itself during the various stages of business development are location not strategic, economy problem, lack of business knowledge and financial support. Moreover, Sarawak Malay entrepreneur terubuk ikan masin also plays an important role in economic development and income growth because terubuk ikan masin is one of the best products in Sarawak besides multilayer cake. Terubuk ikan masin gives contribution especially in eco-tourism. Hence, these terubuk ikan masin are important estuarine fishes, both commercially and culturally in many Asian countries, including Malaysia. The information that generated from this research will be useful for further studies not only for the future researcher but also for sustainable on commercial fish.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".