Profil turysty odwiedzającego Indie
Bibliographic record
Abstract
A systematic growth of tourists visiting India results from a considerable \ninterest in this country. In 2007 in the ranking of Asian countries and the Pacific \nRim countries India was placed on the 11th position (WTO 2009). The analysis of \nthe Indian tourist market enabled to observe some tendencies related to the \nchoice of this country as a destination of international visitors, and due to this it \nwas possible to outline a tourist profile. \nAccording to the Ministry of Tourism in India (2007) as many as 30% of all \narrivals were tourists from Western Europe amounting to 1.6 million. About 1 \nmillion visitors are citizens of North America and Asia. In 2007 among countries \nwhich generated the largest number of arrivals were: Great Britain – 796 \nthousand, the USA – 799 thousand, Bangladesh – 480 thousand and Japan – \n145 thousand. The number of tourists visiting India is still increasing. \nAccording to World Tourism Organisation (2003) about 75% of travelling all \nover the world is performed within the regions and therefore arrivals from Asiatic \ncountries to India can be explained by geographic proximity. A significant share \nof visitors to India from such remote countries as Western Europe and North \nAmerica is likely to be caused by an interest of rich North in poor South which \nhas been observed for several years. \nTourist visits depend also on weather conditions in time of the rest. The first \nand the last quarter of the year are the periods of the highest activity of foreign \ntourists (approx. 30%), the fewest visitors (18%) come to India in the second \nquarter of the year. \nNowadays the world tourism market is observing a trend of decreasing \npopularity of '3S' model (sand, sun, sea) in favour of '3E' model (education, entertainment, excitement) which can be explained by a growing interest in \nIndian culture and exoticism. K. Podemski (2004) states that generally understood \nhuman travelling emerged from the phenomenon of pilgrimages to sacred \nplaces. As many as 34% of foreign visits are motivated by religion. India – the \ncountry where many religions had their beginnings attracts their believers which \nresults in the development of pilgrimage tourism. \nA new form of culture tourism is a film tourism, Bollywood one to be precise. \nAn image of India presented in the world famous film 'Slum-dog' attracts \ninternational tourists, which is confirmed by the increased number (100%) of \nsearched air connections to this country. Moreover, an increased interest in \nvisiting slums has been observed, although it may arouse many controversies. \nIndia is a country for a conscious tourist who is prepared for dissimilarity of \nreality in which the citizens of this country live and in which he will have to \nfunction too. In 2003 a survey was conducted in order to identify associations \nwhich would-be and really travelling to India tourists had with the country \n(Podemski 2004). Respondents (229 persons) answered the following question:” \nWhat are your associations with India?” The most frequent responses were \nstereotypical – they were: tea and elephants. \nHowever, it should be noted that in surveys various elements of the culture \nwere mentioned. They were among others: spot on a forehead, a headscarf, Asia, \nBuddhism, overpopulation, religion, discrimination against women, a turban or \nHinduism. It is likely that the reason of relatively high level of Indian culture \nconsciousness among tourists is the fact that majority of visitors to India are \nmature people who while choosing their destinations made a more well-though \nout decisions. \nA dominating group of tourists, comprising 21%, were people aged 35–44. \nThe remaining two age groups are 45–54 and 25–34, 19.6% and 18% \nrespectively. Ageing of societies, first of all in Western Europe, but also the \nimprovement of health conditions of senior citizens in rich countries leads to the \nincrease in purchasing power of pensioners in a tourism sector. Visitors at the \nage of 55–64 and more than 65 amount up to over 20% of all tourists travelling \nto India. \nAccording to the data published by the Immigration Bureau of India (2007) \nit can be assumed that around 59% of foreign visitors to India are men. \nA significant majority of tourist coming to India are singles. The constitute over \n3/4 of all visitors (Chaudhary 2000). Presumably potential tourists who are not \nmarried can more easily make decisions about remote travels. UNWTO \nprognoses (2009) assume that observable in the modern world loosening of \nfamily bonds will have a beneficial influence on tourism development. The main \npurpose of a trip to India was, first of all tourism (67%), 32% of visitors were \nmotivated by religion, and merely 2% came to India on business (Chaudhary \n2000). No wonder it is said that India can be only loved or hated.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".