Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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 it