Bibliographic record
Abstract
Several Islamic kingdoms once developed in the city of Surakarta, including the Pajang kingdom, Mataram Islam, and Kasunanan Surakarta, leaving behind several historical buildings and objects that have great potential for tourist attraction. If it can be utilized, it will contribute quite a large regional income to the city, considering that the city of Surakarta does not have natural resources and depends on its revenues from the service and trade sectors. The remains of the buildings and historical objects in question can be packaged into cultural tourism and religious tourism. The number of religious tourism objects in the city of Surakarta exceeds the number of cultural tourism, so it has become the attention of the Surakarta City Government to prepare a road map for religious tourism in the city of Surakarta, using primary and secondary data collection methodology with descriptive analysis. Before creating a road map, the first step taken is to identify the potential for religious tourism using SWOT and TOWS analysis. It is hoped that after the potential identification has been prepared, it can become a policy reinforcement in compiling the Surakarta City Religious Tourism Road Map because it will be used by the Surakarta City Government as a guide for the development of Religious Tourism so that it is effective, efficient and targeted in its management.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 0.004 |
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".