Eco-Friendly Tourism Decision Making during COVID-19—Sailing Tourism Example
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In 2020, tourism was highly affected by COVID-19 and its restrictions, such as tourist traffic. Decisions related to trips were made in a state of high risk and uncertainty. This article’s main aim is to present the results of research on decision making by people practising sailing tourism during COVID-19. The survey was conducted in the first quarter of 2021 on 580 sailors from Poland, Germany, Croatia, Italy, France, and Turkey. This is interesting because of the specificity of this form of nautical tourism, which is characterised by high flexibility in the planning and implementation of the cruise. Sailing tourism is also environmentally friendly due to the type of propulsion used and the low noise levels generated. Research has shown that country-specific travel restrictions impact sailing tourism and cruise decisions. The obtained results are important for developing sailing tourism and may contribute to rationalising decisions taken during crises.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it