Key-Factor Strategy of Creative Industry in Distribution Channel: A SWOT Analysis Method
Bibliographic record
Abstract
This study aims to: (1) map the creative industries' strengths, weaknesses, opportunities, and threats in Yogyakarta - Indonesia. The data was collected directly from the source, i.e., primary and secondary data. SWOT analysis is used to analyze the data. The results show (a) the strength factors include: availability of human resources, cheaper living cost, Yogyakarta as a center of culture, tourism, and education; (b) the weakness factors include: low product innovation and creativity, 85.9% of the creative industry do not have a business license, and the creative industry database is not transparent; (c) opportunity factors include: the existence of a creative community, the existence of e-fulfillment (convenience services from JNE), and friendly logistics (digital marketing, warehousing, order fulfillment, technology development, shipping management, and delivery); (d) threat factors include: the existence of an ASEAN free market, namely the Asean Economic Community and product patents (trademarks). The Yogyakarta creative industry should carry out the strategies including (a) the development of the creative industry market; (b) creative industry market penetration; (c) creative industry product development; d) integration into the future; (e) backward integration; (f) horizontal integration, and (g) diversification related to creative industry products.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".