COVID-19 Lockdown and Visiting Friends and Relatives Travellers: Impact and opportunities
Bibliographic record
Abstract
Tourism is regarded as a powerful force in the rise of pandemic diseases as the movement of people is seen as a pathway for the spread of such diseases. The sector is thus susceptible to measures to prevent the spread of pandemics. In the wake of COVID-19, unprecedented lockdown regulations relating to travel restrictions and social distancing have had a direct and indirect impact on the tourism industry and visiting friends and relatives (VFR) travel in particular. More than half of the domestic tourism market comprises tourists who visit friends and relatives in all corners of South Africa. With the restrictions on public gatherings and travel in the country, inter/intra provincial travel largely ceased on 26 March 2020. This paper draws on existing literature, as well as current media sources to review the literature on the legacy of VFR travel; assess the impact of COVID-19 on VFR travel; and finally, to examine the opportunity that might be created by COVID-19 for such travel. It argues that, in the wake of COVID-19, VRF has the potential to fuel the resurgence of the tourism industry in South Africa, especially domestic tourism. Thus, destination marketing organisations might consider a coordinated effort to market this form of travel.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".