Criteria for the Selection of Tourism Destinations by Students from Different Countries
Bibliographic record
Abstract
Abstract The objective of this paper is to identify selected aspects of the management of information about prospective tourist destinations by young people (students) from Canada, Poland, and Trinidad and Tobago. On the basis of a questionnaire study, the ranking of preferences of respondents (i.e., the main criteria of destination choice) has been presented. Students were selected as respondents - as a “convenient sample” - in this privately funded study. A variety of aspects related to comfort (and convenience) and attractiveness have been identified as most important to the choice of destination. These are also leading motives that may form a platform for advertising campaigns and suggestions for regional development. This examination has been done mainly with the use of analysis of averages, Spearman correlation coefficients, and various approaches to factor analysis. It turns out that despite very different characteristics of respondents from the three countries, both their preferences and motives for promotion of the destination are very similar. Conclusions can be helpful for travel agencies and those responsible for the development of tourism infrastructure, as well as for the organization of further studies on the subject. The combination of various statistical tools used when examining the subject and the finding - that is, the similarity of preferences between travelers - can be regarded as new value when examining the subject.
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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 | 0.011 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".