Exploring the Visiting Friends and Relatives (VFR) Tourism From the Hosts’ Perspectives in the Context of COVID-19 Pandemic
Bibliographic record
Abstract
This qualitative research explores the experiences of Bangladeshi immigrants in the Greater Toronto Area (GTA) relating to the hosting of Visiting Friends and Relatives (VFR) especially with the context of the COVID-19 pandemic. As VFR tourism is prominent in communities with growing immigrant populations, data have been collected from Bangladeshi adult immigrant communities living in the Greater Toronto Area (GTA) to get in-depth ideas about their hosting experiences. The study relied on primary data which is collected through online semi-structured interviews. The result shows that before the COVID-19 pandemic, immigrants frequently hosted the local and international visitors to lessen their loneliness, to show their lifestyle and achievement to the visitors, and to assist new immigrants to settle but due to the pandemic, the immigrant hosts have changed their hosting style to ensure the safety of their visitors and themselves. They hosted only their domestic VFRs when the government allowed them to socialize with a limited number of people, and they did not provide any accommodation. They invited a small group of people for a few hours in an outdoor location. The immigrant hosts have mixed opinions regarding their future hosting. A larger portion of the respondents expressed their intention to go back to their earlier way of hosting whereas the other group found inviting small groups for a few hours was comfortable and less burdensome. The findings of the study suggested both practical and academic implications. Although some studies have shown that VFR tourism contributes to the tourism industry during disasters or crises in a particular region, while other forms of tourism decreased, the role of VFR tourism in the recovery of the worldwide COVID-19 pandemic has not been investigated. Further studies on different communities may provide a bigger picture of hosting experience to contribute to the tourism industry by providing strategic direction and also to the academia by extending the existing knowledge in the field of tourism study.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".