Comparative Study of Selecting Tourist Destinations Abroad: A Case Study of Antalya and Dubai Cities
Bibliographic record
Abstract
Planning for the development of tourism requires development and attention to needs, characteristics and demands of the market as the factors demanding tourism. In this regard, paying attention to tourists’ views, opinions, and motivations for travelling to a destination is of great importance as a necessity of marketing and tourism development planningas well as the basis for designing infrastructures related to tourism. Thus, many countries in a very close and intense competition are looking for increasing their benefits and revenues from this international activity. Several reasons are effective in the development and differentiation of tourist destinations or leaving former famous destinations by tourists and make one city more successful than others. This study aimed at identifying factors effective in selecting tourist destinations of Antalya and Dubai cities. The research method is descriptive-analytic. The statistical population was all the people who were traveling to tourist destination cities of Antalya and Dubai in the spring of 2014. The results show that there is no significant difference between Iranians’ motivation to travel to tourist destinations of Antalya and Dubai and the most important motivation and purpose of the passengers travelling to both destination of Antalya and Dubai were relaxation and recreation (using beautiful beaches and water recreation). In addition, the prominent role of costs and variety of attractions can be highlighted in selecting tourist destinations.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".