TRANSFORMATION OF THE TOURISM INDUSTRY AS A CONSEQUENCE OF COVID-19
Bibliographic record
Abstract
The article examines the changes and consequences in the field of tourism under the influence of the pandemic. Based on the analysis of the report of the World Tourism Organization UNWTO, it was found that in the first ten months of 2020, the number of international revenues decrease by 72%, due to travel restrictions, low consumer confidence and the global fight against COVID-19. in the first quarter of 2020, there was already a 22% reduction in travel, and arrivals in March fell to 57% in all markets. That means a loss of 67 million international tourists and about $ 80 billion in revenue. It is determined that Ukraine lags far behind in the implementation of measures to support tourism, so the forecasts for competitiveness are disappointing. The share of tourism contributions to the economy of Ukraine, according to various estimates, is about 7-10%, and the tourism-related economy in Ukraine depends mainly on domestic tourism and the domestic component of outbound tourism. The results of our own survey were analyzed, on the basis of which (107 respondents mostly from Sumy) it was determined: respondents are amateur travelers, so after quarantine 50% of them will travel as before, without much changes; people consciously and responsibly approach the rules of life during a pandemic, so they are ready to refrain from traveling for some time for the sake of safety of life and health, or travel in compliance with all safety measures, or will choose travel on their own; the main factor when choosing a trip is its cost; respondents are interested in the development of tourism in Ukraine and in their regions, ready to support the development of this area. Moreover, an analysis of the Booking platform research was conducted and their forecasts for tourism trends in 2021 were singled out. We formed ways to solve the problems of tourism in Ukraine: the restoration and proper condition of natural resources, historical and cultural heritage of the country; qualitatively and with interest to inform about tourist places of the country, to launch advertising campaigns; by the authorities - to provide financial and legal support to the tourism industry; support of domestic air and rail connections, as well as regulation of ticket prices, etc.
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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 | 0.001 | 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 | 0.000 | 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) | 0.011 | 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".