Tourism Cohabiting with a Pandemic: Lessons from the COVID-19 (2020–2023)
Bibliographic record
Abstract
The pandemic caused by the striking transmission of COVID-19 in early 2020 decimated the population by attacking the most vulnerable—those with chronic health problems and the elderly: one of tourism’s most important clientele. Before the authorities had the tools to treat and protect the population from this virus, unprecedented sanitary measures were imposed in most countries of the world, restricting freedom of movement—the core of the tourism experience. If no economic sector was spared, tourism was among the hardest hit. As a luxury product, tourism was the first sector to suffer the repercussions of political, economic, environmental and health crises. Economic downturns usually result in layoffs and loss of revenues. The COVID-19 crisis also led to the partial “destructuring” of the tourism industries. In those circumstances, the challenge was to maintain just enough tourism activity to save the enterprises and services involved while preventing the spread of the disease any further. The crisis exposed the fragility of the tourism industry’s capability to adapt and cope with a sanitary crisis. Based on experiences identified in the literature during the pandemic, this study proposes an overview of the adaptation strategies deployed by the tourism industries. The study aims to pinpoint resilient avenues for dealing with future health crises.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".