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Record W2642897920 · doi:10.22004/ag.econ.253212

The Empirical Analysis of Terroir Versus Wine Pricing Relationships - The Case of the BC VQA Wines from the Okanagan and Similkameen Valleys

2017· article· en· W2642897920 on OpenAlexaboutno aff
Kate Pankowska

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicWine Industry and Tourism
Canadian institutionsnot available
Fundersnot available
KeywordsTerroirWineWinemakingVineyardWineryGeographyCorkHumanitiesArtArchaeologyBotany

Abstract

fetched live from OpenAlex

The British Columbia (BC) wine industry is a puzzle because of the juxtaposition in classification of BC as a New World wine-producing region, with its winemaking and vineyard management approach that resembles the Old World wine-producing countries. Unlike the New World wine regions, the BC wine industry doesn’t build around a flagship grape variety, but around the uniqueness of terroir 2. The latest developments on the policy side, including wine industry plebiscite (spring/summer 2016) proposed the establishment of new appellations (4) and sub-appellations (16). Currently, wineries in BC produce wines from grapes that not always come from terroir associated with the location of their estates suggesting terroir heterogeneity. Knowing that specifics of terroir influence grapes quality and as a consequence wine quality, and being aware that quality of wine is correlated with its price an interesting research question arises: Does terroir matter for wine pricing in case of the Okanagan and Similkameen Valleys Vintners Quality Alliance (VQA) wines? In particular, this research investigates how various terroir elements influence wine price formation of the selected BC VQA wines. L'industrie du vin de la Colombie-Britannique (C.-B.) ressemble à un casse-tête dû à la juxtaposition de la classification de la C.-B. comme une région productrice de vin du Nouveau Monde, avec son approche de gestion des vignobles et de la vinification qui ressemble aux pays producteurs de vin du Vieux Monde. Contrairement aux régions vinicoles du Nouveau Monde, l'industrie du vin de la C.-B. ne se construit pas autour d'une variété de raisins vedettes, mais autour de l'unicité d'un terroir2. Les derniers développements du côté politique, incluant le plébiscite de l'industrie vinicole (printemps/été 2016), proposent l'établissement de nouvelles appellations (4) et sous-appellations (16). Actuellement, les entreprises vinicoles de la C.-B. produisent du vin à partir de raisins qui ne proviennent pas toujours du terroir associé avec la localisation de leurs domaines ce qui suggère une hétérogénéité du terroir. Sachant que les spécificités du terroir influencent la qualité des raisins et par conséquent la qualité du vin, et reconnaissant la corrélation entre la qualité du vin et son prix, une intéressante question de recherche se pose: le terroir importe-t-il pour déterminer le prix du vin dans le cas des vins Vintners Quality Alliance (VQA) des vallées de l'Okanagan et de Similkameen? Plus particulièrement, ce projet étudie comment différents éléments du terroir influencent la détermination du prix des vins BC VQA sélectionnés.

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)0.005
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.471
Threshold uncertainty score0.948

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.002
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.133
GPT teacher head0.304
Teacher spread0.171 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

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