Advokasi Kesiapsiagaan Bencana Gunung Api Berbasis Budaya dan Teknologi: Cerita Gunung Seulawah dan Gunung Fuji pada Festival Bunkasai USK 2025
Bibliographic record
Abstract
This community service project focuses on disaster advocacy in Aceh, Indonesia, by drawing lessons from Japan’s experience with Mount Fuji. The subject of dedication is the local community and university students in Banda Aceh, particularly those with limited knowledge and preparedness regarding the volcanic hazards of Mount Seulawah. The aim of this program is to raise disaster literacy, strengthen awareness, and encourage proactive preparedness through an innovative advocacy model that combines technology and cultural approaches. The methodology of this program involved several stages: information gathering and literature review about Mount Seulawah and Mount Fuji; team discussions and planning to design advocacy content; interactive media displays such as VR application “MSeulawah”, 360-degree videos, banners, brochures, and cultural arts; and public advocacy conducted during the Bunkasai Festival at Syiah Kuala University. Tactics included the use of cardboard VR for immersive experiences, video screenings, interactive games to test disaster knowledge, and cultural elements such as Japanese art and traditional games to increase community engagement. The outcomes demonstrate that the integration of technology and cultural strategies was effective in attracting participation, especially among students. Visitors reported an increase in knowledge and awareness of volcanic risks, while the interactive media successfully enhanced retention and engagement. This initiative highlights the potential of hybrid advocacy models to strengthen community resilience and suggests the establishment of a Seulawah Museum as a long-term educational platform.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.003 |
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".