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Record W2329728834 · doi:10.1177/1356766715589621

Preferred travel experiences of foodies

2015· article· en· W2329728834 on OpenAlexaff
Tommy D. Andersson, Donald Getz, Sanja Vujicic, Richard Robinson, Alessio Cavicchi

Bibliographic record

VenueJournal Of Vacation Marketing · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCulinary Culture and Tourism
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsRespondentFocus groupMarketingRecreationAdvertisingResidenceTourismPromotion (chess)Sample (material)Scale (ratio)PsychologyBusinessGeographySociologyPolitical science

Abstract

fetched live from OpenAlex

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.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.637
Threshold uncertainty score0.101

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.059
GPT teacher head0.257
Teacher spread0.199 · 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 designObservational
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

Citations36
Published2015
Admission routes1
Has abstractyes

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