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Record W4282546152 · doi:10.3390/su14127012

Consuming Location: The Sustainable Impact of Transformational Experiential Culinary and Wine Tourism in Chianti Italy

2022· article· en· W4282546152 on OpenAlexaff
Darcen Esau, Donna Senese

Bibliographic record

VenueSustainability · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCulinary Culture and Tourism
Canadian institutionsOkanagan University CollegeUniversity of British Columbia, Okanagan Campus
Fundersnot available
KeywordsTransformative learningExperiential learningTourismSustainabilityContext (archaeology)WineSustainable consumptionTransformational leadershipNarrativeThematic analysisFocus groupMarketingSociologyQualitative researchPublic relationsPedagogyPolitical scienceBusinessGeographyArtSocial scienceVisual arts

Abstract

fetched live from OpenAlex

Tourists visit wine and culinary destinations for unique, geographically indicated experiences that are place specific. The objective of this research is to understand how the transformational potential of experiential wine and culinary tourism best promotes sustainability in the context of international educational travel. Our case study in the iconic Chianti Region of Italy applies a ‘Hopeful Tourism Enquiry’ perspective and focuses on participatory, co-transformative learning, and mindful sustainability. A mixed qualitative research strategy was implemented that integrates the results of in-depth interviews with industry experts, excerpts from expository travel journals simultaneously captured during the experience, and focus group dialogues with participating students at the end of the field course. This case study revealed three overlapping thematic results that illustrate the influence of experiential educational tourism on the sensory and cultural experience of sustainable food and wine to produce co-transformative learning. The co-creation of memorable experiences establishes a unique sensual representation of provenance through the interaction with the region through narrative so that not only is the food and wine being consumed, but also the consumption of place through the storyscape of a positive and memorable experience.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.274
Threshold uncertainty score0.530

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.007
GPT teacher head0.239
Teacher spread0.232 · 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

Citations19
Published2022
Admission routes1
Has abstractyes

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