A REVIEW OF COVID 19’s EFFECT ON HOSPITALITY AND TOURISM SECTORS IN INDIA DURING PANDEMIC PERIOD
Bibliographic record
Abstract
The hospitality and tourism industry is considered as a backbone for growth of any economy, especially in developing countries like India. COVID-19 pandemic has impacted almost every industry in the world, however its adverse impact on the hospitality and tourism sector have been unseen or unheard before. In India, the hotels and hospitality sector has heavily declined in the first quarter of 2020 because COVID-19 has impacted various segments of this sector. The nationwide lockdown has closed hotel and travel sectors, which block all their earnings sources of the industries. To come out from this horrible situation, hotels and tourism sector of India has to frame new strategies in the near term and prepare for the future. From the various study, it has been noted that hospitality and tourism sector of India has affected significantly due to COVID-19. The industry has shown large-scale cancellations of travel bookings and hotel accommodations. A notable number of workers of the industries had lost their jobs due to this crisis. Economy of the country as well as individuals were affected adversely due to this pandemic. Present paper evaluates the impact of the COVID-19 (Corona virus disease-2019) pandemic in India’s hospitality and tourism industries during COVID-19 outbreak period with comparison with normal situation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".