Impact of Globalization Crises on the Tourism and Hospitality Economy
Bibliographic record
Abstract
The global hotel and restaurant industry is influenced by the travel and tourism industry, that contributed \n$8.81 trillion to the global economy in 2018. But today these spheres already indicate significant financial losses due to the coronavirus pandemic (COVID19) in the first quarter of 2020 to a fraction of what \nthey were in 2019. Available data points to a doubledigit decrease of 22% in Q1 2020, with arrivals in \nMarch down by 57%. This led into a loss of 67 million international arrivals and about $80 billion in \nreceipts. The impact of coronavirus on world tourism and the predictions of scientists and pro-government \nstructures are based on previous experience of such crises, such as the spread of SARS or H1N1 viruses. It \nis forecasted that global travel and tourism revenue due to COVID-19 in 2020 will be $447.4 billion, \nglobal employment loss in the sphere – 100.8 million, region with the largest loss in travel and tourism is \nEurope.
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How this classification was reachedexpand
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".