Virtual Reality Design as Digital Learning Media in Preserving Local Culture of Tarawangsa Art
Bibliographic record
Abstract
Civilization and culture become significant entities in the historical development of human life. The rapid development of information and communication technology (ICT) has changed the structure of social life in society, including the preservation of Ormatan arts. One of the practical efforts in the preservation of the art of Ormatan is through the media of documentation that utilizes the sophistication of today's technology. Tarawangsa art, as Sumedang's original art, began to be rarely performed by the young generation in the village of Rancakalong in particular and Sumedang Regency in general. Therefore, an interactive documentation media is needed to be able to be used as a reference and perpetuation of the Ormatan Tarawangsa art to remain sustainable. One such media is virtual reality (VR). The purpose of designing VR media is to help cultural and cultural actors to have an effective media in socializing the native culture of a particular area so that the moral values contained therein can be sustained. The research method conducted by using a qualitative approach involving Ormatan Tarawangsa art figures and virtual reality media practitioners in a direct participatory manner. The results show that virtual reality media can provide convenience in practising Ormatan Tarawangsa art, which is on the brink of extinction for the younger generation.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".