The Roles of Government Funding in Enhancing the Competitiveness of Small and Medium-sized Enterprise in Sabah, Malaysia
Bibliographic record
Abstract
This study was conducted in order to determine the roles of government funding in enhancing Small and Medium-Sized Enterprises in Sabah, Malaysia. Specifically, the study is focused on the Small and Medium-Sized Enterprises in Sabah, Malaysia which were funded by many government-linked agencies such as Yayasan Tekun Nasional, Amanah Ikhtiar, Koperasi Pembangunan Desa and Agro Bank. This qualitative study was carried out in the rural and urban areas in Sabah, Malaysia. The findings of this study revealed that there are several main roles of government funding to enhance the competitiveness of Small and Medium-Sized Enterprises in Sabah, Malaysia. These four main roles were namely provision of access to capital for opening and improving the business, provision of access to capital for skills and knowledge development, improvement of entrepreneurs’ relations, and promoting entrepreneurship and reducing “fear of failure”. In accordance, it can be concluded that the government funds are instrumental in the success of the efforts to enhance the competitiveness level of the so-called Small and Medium-Sized Enterprises in Sabah, Malaysia. Besides, it is highly recommended that state government and all parties (the stakeholders) should specifically implement efforts to fund the Small and Medium-Sized Enterprises as well as to improve their level of competitiveness.
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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 | 0.002 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".