Impact of the Covid-19 coronavirus pandemic on tourism facilities in the regions of Slovakia in 2020
Bibliographic record
Abstract
Tourism is an inter-ministerial sector, significantly affecting the employment and development of regions. The paper aims to determine the impact of the epidemiological situation caused by the COVID-19 on the development of tourism in the regions of Slovakia based on the use of quantitative methods. Extensive travel restrictions caused a record drop in accommodation visit rate in 2020. The number of foreign visitors decreased by two-thirds year-on-year to the level of 1998. The visit rate in the Slovak Republic was mainly by domestic visitors. Despite the pandemic, in the third quarter of 2020, they exceeded last year's record numbers from the summer season. After considering the visit rate of domestic and foreign visitors, the number of visitors decreased the least year-on-year in the Žilina Region. The most significant year-on-year decrease in visitors was recorded in the Bratislava Region, where business clients were significantly absent. Gross sales decreased by almost half compared to the previous year. The highest gross sales were achieved by accommodation establishments in the Žilina Region. The number of overnight stays decreased year-on-year in all regions. However, the length of stays was significantly extended in the fourth quarter of 2020, thanks to the visit rate in spa towns.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".