The Impacts of COVID-19 on the Visitor Attendance of Cultural and Natural Heritage: A Case Study of the South Moravian Region
Bibliographic record
Abstract
Tourism is one of the world’s most affected sectors by the impact of the COVID-19 pandemic. This article deals with the assessment of the impact of COVID-19 on the visitation of the South Moravian Region, including important cultural and natural sites, based on the analysis of empirical statistical data in the last decade and the calculation of the year-on-year change in attendance between 2019 and 2022. According to the results, the number of visitors to the South Moravian Region in 2020 fell by almost half, including a decrease of a quarter of visitors to cultural monuments compared to 2019. On the other hand, visits to natural areas with no restricted access increased by a fifth after 2020, but natural areas with restricted access fell by more than 40%. From 2021, attendance of the South Moravian Region began to increase slightly, and in 2022, it reached ninety percent of the level before 2019, including attendance at cultural and natural sites. The results of the research confirmed the growing trend in visitors to the South Moravian Region, including cultural and natural monuments, which were significantly influenced by the impact of COVID-19 on tourism after 2020, with a recovery of tourism in 2022.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".