Strategies and policies for developing SMEs based on creative economy
Bibliographic record
Abstract
Small and Medium Enterprises (SMEs) play a major contribution to the Indonesian economy. Along with the development of a centralized economic direction on consumers, the use of technology in all fields, and information transparency, SMEs must also be able to adapt in the era of the industrial revolution 4.0. This research aims to develop strategies for strengthening and developing SMEs and mapping the hierarchy policy of developing a creative economy-based SME business model in the era of the industrial revolution 4.0 in the Province of Bali. The data in this study were collected through documentation, FGD, and interview techniques, then analyzed using SWOT and MULTIPOL analysis techniques. The ability of creative economy-based SMEs to compete in the global era depends on internal and external factors. The analysis shows that SMEs in the Province of Bali are in a position of growth and built, so the strategies adopted are intensive strategies or integration. Development policies for SMEs, especially in the era of the industrial revolution 4.0, need to be directed so that the guided SMEs become independent SMEs. The policy package for the development of target SMEs includes technology, capital, marketing and infrastructure policies.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".