The Impacts of the COVID-19 Pandemic on the Tour Operator Market—The Case of Slovakia
Bibliographic record
Abstract
The aim of the research is to determine the impact of the COVID-19 pandemic (and subsequent state aid) on selected financial indicators of tour operators operating on Slovakia’s market. The article analyses the changes in the market between 2018 and 2020 (market concentration, insolvency insurance). For the purposes of describing the financial position of tour operators, the medians of selected financial indicators were processed. At the same time, a two-sample t-test was used to test the hypotheses of the medians of these indicators for tour operators with a valid insolvency protection contract and without such a contract. The Herfindahl–Hirschman index was used to quantify the impact of the pandemic on the tour operator market concentration ratio. The state aid provided prevented tour operators from going bankrupt. Based on the Herfindahl–Hirschman index, we can say that there was an increase in concentration in this market. At the same time, however, there was a decline in profitability and an increase in their Liabilities to Assets ratio. However, currently insured tour operators do not have higher values of these indicators. These data are important for the discussion on the legal regulation of the protection of tour operators against insolvency.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".