Recovery of Post-Pandemic Tourist Visits Rates Through Geopark Destination Attributes
Bibliographic record
Abstract
Purpose – The Pongkor National Geopark has a geological peculiarity in the form of an underground gold mine that distinguishes it from other geoparks in Indonesia and the world. The Pongkor National Geopark raised the theme of the evolution of the quarter magmatic arc associated with Pongkor gold mineralization as an icon of geological heritage known to the world. The presence of Geopark status shows that the Pongkor area has prominent geological elements and fulfils archaeological, ecological and cultural values and is able to encourage and empower local communities to contribute in maintaining and improving the function of natural heritage so as to contribute to economic development. However, the COVID-19 pandemic has reduced tourist visits as a consequence of the government's efforts to reduce the spread of the outbreak through closing and restricting tourist areas. The purpose of this study was to analyse the effect of destination attributes on the intention of tourist loyalty in the Pongkor National Geopark Area. The method used in this research is verification with the population, namely tourists who have visited the Pongkor National Geopark at least 1 time and are willing to make visits in the future. The analytical method used is Spearman rank correlation, coefficient of determination, and t test with a significance level of five percent. Findings – This research supports previous studies where there is an influence given by the attributes of the destination on the intention of tourist loyalty. The magnitude of the effect given by the destination attribute on loyalty intentions is 57.6%.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".