Transforming Tourism Destination Management: Response to Natural Disasters and the COVID-19 Pandemic - A Case Study of Sembalun Geosite in Indonesia
Bibliographic record
Abstract
Sembalun is a tourist destination in Rinjani Lombok UNESO Global Geopark which made full with tourists, either local, domestic, or foreign.The 2018 earthquakes, which were followed by the COVID-19 pandemic, became the momentum of transformation of tourism development there.This research aims to investigate the transformation of tourism destination management in Sembalun Geosite following the 2018 earthquakes and the COVID-19 pandemic.This research uses a qualitative research method with a case study approach.Indepth interviews and participatory observation are primary data collection techniques and literature study is a secondary data collection technique in this research.The transformation was applied as an effort to improve the tourism management based on an evaluation from the DMO, as a response to the implementation of tourism principles in the new normal era, and an effort to make the tourism development in Sembalun Geosite fully sustainable.The transformation was implemented by: (1) changing the orientation of tourism development, from quantity to quality; (2) distributing tourist visits as even as possible; (3) encouraging the tourism management to play their roles orderly and thoroughly; (4) developing tourism based on preservation, not alteration; (5) developing strategies for unique tourism developments; and (6) fostering collaborations among tourism destination organizers in Sembalun Geosite.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".