“A Study On Financial Performance Of Mahindara Holidays & Resorts India Limited”
Bibliographic record
Abstract
According to The World Travel and Tourism Council (WTTC), in the year 2019 the Indian travel and tourism industry contributed 6.8 per cent to GDP. The contributionto employment in India was 8 per cent or approximately 40 million jobs. The traveland tourism industry has to work tirelessly with our partners and the TouristDepartment, Government of India to increase tourism so that we can increase ourforeign exchange earnings and create more jobs. In 2019 the number of foreign visitors to India totalled 11 million. Over 2.5 million foreign visitors arrived inIndia on the tourist e-visa scheme, a growth of 24 per cent over the previous year. Foreignvisitors from The United States of America contributed 9 per cent, the United Kingdomcontributed 6 per cent and Canada and Australia contributed 2 per cent each. However, this share of foreign travellers visiting India relative to other Asian countries continuesto disappoints. The Indian travel and tourism industry has to work much harderto attract more foreign visitors to our country.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".