Brand Association of Ciletuh - Palabuhanratu Geopark Towards COVID-19 Pandemic and Sustainable Tourism
Bibliographic record
Abstract
The purpose of this research is to investigate brand association of Ciletuh – Palabuhanratu UNESCO Global Geopark towards the COVID-19 pandemic and sustainable tourism. This research employs a qualitative research method with a case study and descriptive statistics model. The data used in this study are primary and secondary ones in which the techniques of data collecting is by observation, purposive random sampling with Likert scale, as well as literature studies. The results of this study show that Ciletuh-Palabuhanratu Geopark is not only a strategic place for tourism activities in the COVID-19 pandemic since it has characteristics to comply health protocols but also able to meet the tourism recovery during the pandemic. The tourism activities provided by Ciletuh-Palabuhanratu Geopark sites meet the concept of quality adventure tourism and in line with sustainable tourism with concerns on balancing the environmental conservation, local economic empowerment, as well as local social and culture preservation. The brand association of Ciletuh-Palabuhanratu Geopark is shown by its tourism product scope and quality which are associated to sustainable tourism, moreover the use occasion is line with the COVID-19 pandemic situation. The attributes of UNESCO Global Geopark also create the values of tourism activities during and after COVID-19 pandemic which meet the points of sustainable tourism activities and recoveries.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".