INVESTIGATING THE EFFECTS OF COVID-19 ON TOURISM IN THE G7 COUNTRIES
Bibliographic record
Abstract
Natural and human-made crises can significantly impact the development of countries’ tourism industries. The susceptibility of countries to these crises depends on their policies, planning, and management in facing diverse challenges. This article aims to investigate the effects of the COVID-19 pandemic on the tourism industry in G7 countries by comparing rankings and positions on indices in 2016 and 2020. Data collected from the RANking COMparison (RANCOM), Proximity Indexed Value (PIV), and Double Normalization Compromise Ranking of Alternatives from Distance to Ideal Solution (DNCRADIS) models have been utilized for data analysis. The research findings indicate noticeable differences in using different models, as the rankings and positions of G7 countries for the years 2016 and 2020, except for two countries, the United States and France, have been different. The research results demonstrate that the COVID-19 crisis had significant impacts on the tourism industries of G7 countries. Countries like the United States, France, and the United Kingdom appear as leading nations in the tourism industry, while Japan and Canada faced challenges, and Germany and Italy experienced changes in their positions. Based on these results, officials and planners in the tourism industry of G7 countries can make appropriate decisions for the development and improvement of tourism under similar crisis conditions. Moreover, these findings can serve as a valuable guide for other countries in managing similar crises in the tourism industry.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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 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".