Preferred travel experiences of foodies
Bibliographic record
Abstract
A large-scale sample of food lovers accessed by an online survey, which followed a qualitative focus group study, employed the photo elicitation technique to investigate their preferences for travel experiences. This technique identified top choices both for food-related and other types of urban, nature-oriented and active recreational pursuits. Overall, the most popular experience sought was described as ‘enjoy regional cuisine in a local restaurant’ and depicted a couple dining informally with a waterfront backdrop. The photo conveyed the romantic, authentic and informal messages all at once. More detailed analysis revealed significant differences according to respondent country of residence and previous food-related travel. Specifically, the most experienced food tourists were the most likely to select food festivals and meeting/learning from chefs. Those who had travelled less for food experiences had more general, leisure-oriented preferences that included nature and heritage. Results have implications for precise targeting at food tourists; the packaging of experiences; and destination development, branding and promotion. A number of methodological and theoretical issues are discussed, including the issue of how photos communicate messages and their use in marketing.
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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 | 0.001 | 0.000 |
| 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) | 0.006 | 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".