Picture it: The Use of Food-Related Images in Tourism Visitor's Guides
Bibliographic record
Abstract
Culinary tourism has become an important means of attracting quality visitors for many destinations. As tourism destination marketers develop the brands and associated promotional material through which to communicate with potential visitors, the need to identify ways to differentiation their place brand vis-à-vis the place brand of other tourism destinations becomes paramount. One way to engage potential visitors via tourism promotional material is through the use of images. By examining the food-related imagery used by specific Canadian tourism destinations, the increased focus on culinary tourism and the evolution of the use of imagery can be seen more clearly. This study offers a contribution to tourism research by examining the evolution of culinary tourism promotion in three geographically-linked regions through the use of food-related imagery in their tourism visitor's guides. The findings of this study can also offer valuable information to tourism industry stakeholders who have identified culinary tourism as a priority and want to further develop their positioning, differentiation, and marketing strategies using food-related imagery.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".