Is travelling currently a risk? The impact of COVID-19 and war in Ukraine
Bibliographic record
Abstract
The aim of the study is to examine how regular travellers’ behaviour and views regarding international travel have changed as a result of the Covid-19 pandemics and the war in Ukraine. The study includes three research questions 1) how much is travel missed and how do pandemics affect international travel behaviour? 2) how do regular travellers view the danger associated with pandemics and the war in Ukraine? 3) to what extent are future travels perceived as risky? The research is based on statistical data on outgoing tourism and domestic visitor monitoring data analysis, and interviews with 33 regular Latvian travellers. The interviews were conducted during the first quarter of 2022. According to the findings, regular travellers currently feel a lack of excitement about planning trips or international travel experiences. Those who frequently travel for business report a lower level of longing for this experience. The habit of travelling locally has increased due to the lack of international alternatives during the pandemic with restrictions on global mobility. Regular travellers accept the inconveniences caused by the Covid-19 restrictions, but they were afraid and applied self-protection strategies, or even refused to travel entirely, during the first month of the war in Ukraine. Finally, regular travellers are not going to refrain from taking multiple journeys overseas or to Western European countries in the next six months but will avoid travels to zones of military conflict.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".