Travelling During The COVID-19 Pandemic
Bibliographic record
Abstract
The research is to study the domestic tourists’ considerations and perceived risks and how they allay the disharmony that occurs between the desire to travel and to stay at home because of the pandemic situation, to their decision making in traveling to Bali. Data were collected through questionnaires to 96 domestic Bali tourists and interview with 5 individuals from them to gain deeper insights on the travel decision-making process between January-February 2022, a point where the COVID-19 case in Indonesia rose once again after its stagnant low case report in the third quarter of 2021. Regression analysis was performed and suggest that there is an impact between the perceived risk on purchase decision-making. In addition, results revealed that for the most part, these domestic tourists tend to set aside their worries about COVID-19 and other safety concerns so that their choice to visit Bali can be more prominent, essentially because of feeling weary with the pandemic situation which makes them feeling the urgency to escape from the “routine”.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".