Geotourisms in Cibenda Village: Potencies of Sustainable Tourism in Ciletuh – Palabuhanratu Geopark after COVID–19 Pandemic
Bibliographic record
Abstract
COVID-19 pandemic, which is still lasting from the end of 2019 until the 1st quarter of 2022, has influenced and altered the tourism paradigms before and after pandemic. The changes are not only adapting health protocols to minimize the pandemic outbreaks but also the tourism reborn from mass tourism into sustainable tourism. This research is investigating the geotourism activities in Cibenda village and its potencies of sustainable tourism found in geosites of the village which are included as geoarea of Ciletuh – Palabuhanratu UNESCO Global Geopark. Qualitative descriptive method with a case study was used in this research. The data used are primary and secondary ones which were taken by observation, open interview with prominent people of Cibenda, and literature studies. This research’s results show that Cibenda village has potential tourism attractions with their geodiversity, biodiversity and cultural diversity providing something to see, something to do and something to learn. Furthermore, the geotourism activities in Cibenda are in accordance with the concept of quality adventure tourism and in line with sustainable tourism principles i.e. balancing the environmental conservation, local economic empowerment, as well as social and culture preservation which comply with the health protocols of COVID-19 and the necessities of tourism activities after pandemic.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".