Addressing the effect of COVID19 pandemic on the Tourism Industry in Haridwar and Dehradun Districts of Uttarakhand, India
Bibliographic record
Abstract
The World Travel and Tourism Council has said “The coronavirus COVID19 epidemic is putting up to 50 million jobs in the global travel and tourism sector at risk, with travel likely to slump by a quarter this year, Asia being the most affected continent”. The predictions being flashed by the world economic forum about the travel & tourism industry is also reflected in North India tourism industry. Hence, the study aimed to address and evaluate the effect of COVID19 on Uttarakhand tourism, especially, on the site of Haridwar and Dehradun as they are the entry to the gateway of Major Char Dham Yatra of Himalaya, and where all India tourists arrive since centuries. The prime focus of the study was to review and investigate the people's reaction towards the pandemic situation and how it had affected the working as well as livelihood of people associated with Tourism and hospitality in this region of Uttarakhand. The study did a online survey through self-prepared 20 questions questionnaire. The study indicates before and COVID19 pandemic affected nearly 60% respondents for their professional working and many were affected with payment schedules. The majority of the respondents (78.4%) were very much positive and had faith for the bright future besides happy to spend time with family. In addition, the study showed a strong response from participants for the need of the change in the service of the tourism industry indicating a change for its survival with the present threat possibly through finding solutions such as local tourism, spiritual collective effort and support.
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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 | 0.000 | 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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".