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Record W4388561603 · doi:10.1079/tourism.2023.0039

From Struggle to Success: The Osoyoos Indian Band’s Journey into Agritourism and Economic Prosperity

2023· article· en· W4388561603 on OpenAlexaffabout
Zainub Ibrahim, Ahmed Jaffer, Fiona McCarthy-Kennedy

Bibliographic record

VenueTourism Cases · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicWine Industry and Tourism
Canadian institutionsAlgonquin College
Fundersnot available
KeywordsWineryProsperityTourismRevenueBusinessIndigenousSWOT analysisGeographyEconomyWineMarketingEconomic growthEconomics

Abstract

fetched live from OpenAlex

Summary In the province of British Columbia, Canada, the Osoyoos Indian Band (OIB) employs agritourism, specifically wine tourism, to serve their community economically and socially. Once on the verge of bankruptcy, they are now one of the wealthiest First Nations in the country, and their participation in the wine industry has played a critical role in this transformation. The OIB has been involved in the wine industry for over three decades. Beginning in 1968 with only 200 acres of vineyard, they currently own and operate one of the largest vineyards in the Okanagan Valley, and an award-winning winery Nk’Mip Cellars – the first Indigenous-owned and operated winery in the world. The winery was part of a broader development plan called the Nk’Mip Project. The project encompassed various planned developments, including the Nk’Mip Desert Cultural Centre which acts as a value-added attraction for those tourists visiting the winery and its vineyards. Economic self-sufficiency through economic development projects is the core belief of the OIB. Chief Louie says that their band has always had a business culture and they have continued to evolve. These developments have allowed the OIB to diversify its revenue streams, protect its lands, and promote its culture and history. Information © The Authors 2023

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.743
Threshold uncertainty score0.511

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0320.015
Scholarly communication0.0120.003
Open science0.0010.011
Research integrity0.0020.007
Insufficient payload (model declined to judge)0.0090.001

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 head0.020
GPT teacher head0.247
Teacher spread0.227 · 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 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

Citations0
Published2023
Admission routes2
Has abstractyes

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