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Authentic Culinary Tourism Experiences: The Perspectives of Locals

2020· article· en· W3107906253 on OpenAlexaboutno aff
Susan C. Graham

Bibliographic record

VenueGastronomy and Tourism · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCulinary Culture and Tourism
Canadian institutionsnot available
Fundersnot available
KeywordsTourismContext (archaeology)SociologyPrivilege (computing)DestinationsAestheticsGeographyPolitical scienceArt

Abstract

fetched live from OpenAlex

Culinary experiences have long been an important aspect of tourism. For many destinations, culinary offerings have become ubiquitous with the place—pasta in Italy, wine in the Loire Valley or Napa Valley, or curry in India. As tourists increasingly seek out authentic touristic experiences, including culinary experiences, the question arises regarding what constitutes an authentic culinary experience in a place. While authentic and authenticity are terms widely used in the tourism literature, a precise definition of what those terms mean and a method for identifying that which is authentic remains elusive. Research regarding authenticity in tourism suggests that locals occupy a "place of privilege" with respect to determining the authenticity of a touristic experience because of their connection to and context in relation to the place. This article examines the perspectives of Prince Edward Island (PEI) residents with respect to what constitutes an authentic culinary touristic experience in which visitors to Canada's smallest province can partake and that provide those visitors with a glimpse of what life in PEI is or was really like, and provides a voice for an underrepresented group in the authenticity discourse. Results show that authentic culinary experiences transcend food, and encompass people, places, and experiences in ways that enrich touristic endeavors. Locals understand and interpret authenticity in ways that both conform to and differ from existing scholarly work related to tourism authenticity, and span objective, existential, and constructive authenticity.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.379
Threshold uncertainty score0.518

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 head0.015
GPT teacher head0.208
Teacher spread0.193 · 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 designQualitative
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

Citations23
Published2020
Admission routes1
Has abstractyes

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