Segmenting and Profiling the Cultural Tourism Market for an Island Destination
Bibliographic record
Abstract
Culture is an important part of the tourism product and is one of the variables that can increase the attractiveness and the competitiveness of a tourism destination. Cultural tourism covers all aspects of travel and provides an opportunity for visitors to learn about a destination’s history and way of life. However, the size and importance of cultural tourism for specific destinations is still a matter of some debate. Some suggest that it is difficult to truly document the size of the cultural tourism market due to the issues of defining a "cultural tourist." This paper examines the magnitude and significance of cultural tourism for Prince Edward Island, a major Canadian tourist destination. In doing so, the paper segments and profiles the tourism market, and identifies distinguishing trip characteristics. Based on the research, two segments of travelers, based on cultural related activities, are clearly evident. The results indicate that culture-seeking tourists and other interest tourists are significantly different in terms of many travel variables, and particularly in spending patterns. For the destination studied, the extra economic impact associated with cultural visitors is substantial due to three factors: a closer personal connection to the destination, a longer overall length of stay, and higher per person per night spending.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".