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Record W1596950671 · doi:10.1108/09547540610681077

Benchmarking wine tourism development

2006· article· en· W1596950671 on OpenAlexaffabout
Donald Getz, Graham Brown

Bibliographic record

VenueInternational Journal of Wine Marketing · 2006
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicWine Industry and Tourism
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsBenchmarkingTourismWineBusinessDestinationsMarketingTourist destinationsProcess managementPolitical scienceFood science

Abstract

fetched live from OpenAlex

Abstract Purpose – This paper seeks to develop a framework for comparisons and benchmarking between wine tourism destinations. Design/methodology/approach – A regional case study was undertaken, including data from a survey of 23 wineries in Canada's Okanagan Valley, British Columbia. The survey provides the winery perspective on development of wine tourism, as well as opinions on what should be done to improve wine tourism. Findings – Wineries were found to be pursuing tourism developments, but kept little data on visitors and related spending. Their goals and opinions on what is needed in the region revealed that they are mostly oriented toward domestic, independent travelers. One hypothesis emerging from this case study is that the growth and increasing sophistication of wine tourism infrastructure, both at wineries and elsewhere in the region, is in large part a function of market potential. On the supply‐side, a critical mass can be facilitated through establishment of major, landmark wineries that are purpose‐built as tourist attractions. Practical implications – Using this profile of the Okanagan, implications are drawn for comparisons and benchmarking among wine tourism destinations, including a suggested process and measures. Research limitations/implications – The single case study limits generalizability to other destinations, and the achieved sample of wineries does not necessarily reflect the major corporate wineries in the Okanagan Valley. More systematic comparison of wine regions is recommended. Originality/value – This research makes an original contribution for applying the concept and method of benchmarking to wine tourism destinations. It is of value to the wine industry, destination marketers, and host community planners.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.014
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.035
Threshold uncertainty score0.103

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.019
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0090.015
Science and technology studies0.0030.003
Scholarly communication0.0130.006
Open science0.0020.007
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.001

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 head0.009
GPT teacher head0.216
Teacher spread0.207 · 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 source (direct Gemma or distilled Codex), 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

Citations111
Published2006
Admission routes2
Has abstractyes

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