Cultural-Heritage Virtual Tour for Tourism Recovery Post COVID-19: A Design and Evaluation
Bibliographic record
Abstract
In tourism, virtual tours are one of the latest promotional trends utilized during the COVID-19 pandemic, especially in keeping potential tourists saturated and interested in visiting the tourist attractions when the 'new normal' conditions become stable. Furthermore, virtual tours are also a part of historical conservation for cultural-heritage tourism. This research aims to design a virtual cultural-heritage tour route in the Kesawan area of Medan city, make widely promoted virtual tour videos, and evaluate the quality of virtual tourism by arousing interest in prospective tourists to visit the cultural-heritage area of Medan city. Descriptive qualitative design and quantitative regression methods are adopted in this research. Qualitative descriptive and qualitative methods were used to explain the trips and measure the impact of virtual tours in the city of Medan, especially the Kesawan district as the research area. The first result showed the design of a virtual tour starting from the itinerary planning process, taking pictures, editing, and publishing on YouTube media. It was also observed that the published cultural-heritage attracts potential travelers to visit and physically experience the tourist attractions. Moreover, the virtual tour design will be enriched with the addition of English subtitles to obtain a larger audience.
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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 | 0.011 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".