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Record W4221094717 · doi:10.33423/jabe.v24i2.5102

Wine and Cultural Tourism as One of Niche Tourism Opportunities in Canada and Slovakia

2022· article· en· W4221094717 on OpenAlexvenueaboutno aff
Marica Mazurek

Bibliographic record

VenueJournal of Applied Business and Economics · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicWine Industry and Tourism
Canadian institutionsnot available
Fundersnot available
KeywordsTourismPromotion (chess)Product (mathematics)ReputationBusinessTourism geographyNiche marketMarketingCultural tourismCultural heritageGeographyAdvertisingEconomic geographyPolitical science

Abstract

fetched live from OpenAlex

The article deals with new development in tourism, especially after the pandemic situation of COVID-19. In the period of economic crises and turmoil tourism places are able to benefit from the positive influence of so called soft and more sustainable values created by the improvement of image, reputation and the quality of destination services, by using of local cultural resources and heritage. The objective of this study is wine tourism, which has become one of the growing niche attractors in specific regions all over the world. In the combination with culinary tourism and cultural tourism offers a specific tourism product not only to the segment of cultural tourists, but tends to be more attractive to the segments of seniors. The study will be focused on two countries, Canada and the case of Brand Niagara Region and Slovakia. The qualitative research has been prepared, especially focused on product development and the innovative promotion strategies. These two countries were chosen due to a personal experience, former research and interest in this topic.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.252
Threshold uncertainty score0.852

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 communication0.0000.001
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 head0.025
GPT teacher head0.185
Teacher spread0.160 · 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

Citations1
Published2022
Admission routes2
Has abstractyes

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