Experiential tourism: Creating and marketing tourism attraction experiences.
Bibliographic record
Abstract
Purpose This chapter investigates the current trend toward both creative and experiential tourism in cities in terms of the development and marketing of local attractions. Methodology/approach Creative tourism in cities is profiled through a literature review and further investigated by means of a case study at a local attraction in Toronto, Canada. The choice of a site was one of a creative city and the re-purposing of a formerly industrial site for visitation. Findings The study of Evergreens Brickworks demonstrated the use of marketing techniques to identify markets and match visitors with experiences. The visitor segmentation method determined that pre-scheduled and bookable activities offered for locals need to be offered on a different basis for tourists, who may be one time visitors to the site. The product-market match process suggested areas in which products could be modified or indeed created. Practical implications This practical study offers lessons for other local visitor attractions and their managers desiring to identify market segments and match them with appropriate activities creating experiential tourism at the site level within the creative city context. Originality/value While many studies of the creative tourism concept and cities have been undertaken within the context of destinations this research offers a site-specific perspective as well as marketing perspective that will be of practical value to attraction managers.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".