An Exploratory Study of Typical and Traditional Culinary Arts in Surakarta and Semarang as Cultural Heritage to Support Indonesian Tourism Industry
Bibliographic record
Abstract
As a three-year-project funded by the Government of Indonesia in support of tourism industry, the current study explored the existence of tradional culinary arts of Surakarta and Semarang, Central Java, Indonesia in an attempt to promote Indonesian tourism industry. A variety of traditional snacks from the two cities were identified to find out the similarities and differences in terms of exclusiveness and flavors. As a qualitative and descriptive research, the data were collected through observation on the types of traditional snacks, and interviews with the vendors with respect to the process of production. The findings showed that Surakarta is rich in traditional snacks, such as Sosis Solo, Jadah Blondo, Intip Goreng, Rambak, and various kinds of Lenjongan. In Semarang, on the other hand, there are Ganjel Ril, Winbgko Babat, Kue Senteling, Wedang Tahu, Lumpia and one type of Lenjongan—therefore Lenjongan can be assumed is the only similar food in the two cities. The snacks from the two cities have distinctive features of flavors that deserve both domestic and international recognition. Therefore these types of snacks can be tourism icons to attrack national and international tourists to visit both cities. In conclusion, the typical traditional culinary arts should be preserved and maintained to support Indonesian tourism industry.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".