Kontribusi Ekonomi Kreatif Dalam Mendukung Sektor Pariwisata Di Kabupaten Sikka
Bibliographic record
Abstract
Sikka Regency is an area with superior tourism potential but the contribution of the tourism sector does not have a significant influence on economic growth, this is due to the low contribution of the creative economy in supporting the tourism sector which is influenced by less attractive and competitive creative economy products, lack of promotion and branding, lack of collaboration and partnership between tourism actors and creative economy actors and lack of support from the government and related institutions. To overcome these problems, the proposed solution is the existence of regulations as a strong legal foundation and framework for policies and programs that support the creative economy in Sikka Regency. The writing of this Policy Paper aims to enable the creative economy to contribute more to the development of the tourism sector in Sikka Regency and increase regional economic growth as well as policy recommendations to overcome the low contribution of the creative economy to support tourism in Sikka Regency. The method used in this paper is problem tree analysis involving literature study, field observation and in-depth interviews with tourism and creative economy actors. The recommended policy option is a Sikka Regent Regulation on Creative Economy that can provide a legal framework and programs that support the development of the creative economy sector in Sikka Regency.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.005 |
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".