PRICING ATTRIBUTES OF WINES FROM EMERGING SUPPLIERS ON THE BRITISH COLUMBIA MARKET
Bibliographic record
Abstract
We examine British Columbia (BC) wine consumers' valuation of wine imported from emerging suppliers (Argentina, Bulgaria, Chile, Croatia and Hungary) using hedonic pricing technique.BC Liquor Distribution Branch retail sales data covering weekly sales of table wine imported into the province of British Columbia from all five countries for the period April 20 th , 2002 to May 8 th , 2004 are applied to estimate the influence of wine attributes on prices.The results indicate that grape variety, brand name, country of origin, and alcohol content are important factors influencing prices paid by consumers.In particular, Chilean white and red wines are associated with larger price premia as compared to Argentinean wines.Wines from Hungary, Bulgaria, and Croatia, although sold in large quantities in the BC market, are substantially discounted in comparison to New World wines.Cabernet Sauvignon fetches a higher price when blended with other varietals and Chardonnay appears to be popular and highly valued by consumers among white wines.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".