“Are there truly acoustic melodies on hillsides?” Can such human-based manufactured soundscapes be an asset to tourism destinations?
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
Soundscape tourism has become one type of tourism, and its trend is emerging in most areas with hilly, forested, and natural landscapes, such as Bantul Indonesia, becoming a mainstay for region development and its community. This article explores four human manufactured soundscape tourism destinations in Bantul, Indonesia, examining the interrelationships between each tourism stakeholder and pinpointed development from a socio-economic perspective. We adopt a cross-case study approach, drawing main sources from government statistics, regulations, social media narratives, and online news. Using the NVivo 12 Plus software, we coded and annotated the research source. Our research revealed that in four case studies, tourism soundscapes emerged as the primary tourist attractions, with other attractions only marginally contributed. Presenting music or acoustic stages enabled tourism industry to reap benefits, particularly for local community and regional income. However, it is important to emphasize sustainability issues, thus, the continuous increased in music soundscape in nature has led to the formation of collaborations among tourism actors, with local communities “Pokdarwis” posed as the principal driving force behind destination development. This study demonstrates that human-manufactured soundscapes have the potential to increase visitor numbers and outperform natural soundscapes in natural destinations.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it