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Picture it: The Use of Food-Related Images in Tourism Visitor's Guides

2018· article· en· W2884613440 on OpenAlexaboutno aff
Susan C. Graham, Elizabeth Toombs, Shannon A Courtney, Hannah Dawson

Bibliographic record

VenueGastronomy and Tourism · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCulinary Culture and Tourism
Canadian institutionsnot available
Fundersnot available
KeywordsTourismVisitor patternMarketingDestinationsPromotion (chess)BusinessAdvertisingTourism geographyDestination marketingTourist destinationsGeographyPolitical scienceComputer science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.293
Threshold uncertainty score0.233

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.021
GPT teacher head0.213
Teacher spread0.192 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2018
Admission routes1
Has abstractyes

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