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Record W4415585903 · doi:10.21083/crrf.v31i1.7340

Cultural sustainability in rural wine and food tourism

2023· article· W4415585903 on OpenAlexaffabout
Danielle Robinson

Bibliographic record

VenueProceedings of the Canadian Rural Revitalization Foundation · 2023
Typearticle
Language
FieldBusiness, Management and Accounting
TopicWine Industry and Tourism
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsSustainabilityRural tourismFood sovereigntyTourismFood systemsTourism geographyEcotourismSustainable tourismRural area

Abstract

fetched live from OpenAlex

Researchers concerned with the complex, interrelated, and multi-scalar relationships between culture and rural tourism development have explored both positive and negative dimensions in diverse contexts; however, more systematic attention to the concept of cultural sustainability is needed to design supportive rural tourism policies and processes. Wine and food tourism is one of the fastest growing rural tourism niches and intersects with critical cultural sustainability issues such as local food systems, food sovereignty and agricultural land use, therefore, it is particularly important to explore cultural sustainability in food and wine tourism contexts. Comparative case studies in two Canadian wine regions, British Columbia's South Okanagan Valley and Nova Scotia's Annapolis Valley, are used to gain a better understanding of the relationships between local food cultures, rural tourism development and sustainability in different provincial contexts with a particular emphasis on the role of related planning, policy and governance. Findings from semi-structured interviews and focus groups provide insights into how culturally sustainable food and wine tourism is conceptualized, recognized, developed, supported and promoted in each case. Processes and policies that support cultural sustainability are discussed and future research is proposed. This research is generously supported by a Rural Policy and Learning Commons (RPLC) Research & Exchanges Service Rural Policy Research Grant

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.061
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0010.000
Scholarly communication0.0010.004
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 head0.014
GPT teacher head0.235
Teacher spread0.220 · 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.

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

Citations0
Published2023
Admission routes2
Has abstractyes

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Same venueProceedings of the Canadian Rural Revitalization FoundationSame topicWine Industry and TourismFrench-language works237,207