Bibliographic record
Abstract
Researchers in culinary tourism often implicitly treat visitors interested in culinary products as a relatively homogeneous market. Using data obtained from the Canadian Travel Activities and Motivations Study, three a priori segments are defined: visitors who participate only in food-related activities, those who participate only in wine- related activities, and those who participate in both. The food segment was the largest of the three, with nearly 25% of respondents fitting this category; wine was the smallest segment with less than 4%. Wine and food accounted for about 7%. The food segment had a higher proportion of females than the other segments, with lower average educational attainment and lower incomes. Wine-oriented visitors were more balanced between male and female, had average ages and educational attainment, and higher incomes. Those visitors involved in both sets of activities were predominantly male, older, had the highest educational levels, and much higher incomes. Trip motivations and activities also differed significantly among the three segments with the food and wine segment showing the greatest diversity of motivations and activities. In other words, there are distinct types of culinary tourists who seek distinct types of culinary experiences. Different methods of communications, and different packaging and product development strategies need to be employed to reach each of the segments identified here.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".