Determinants of Wineries’ Decisions to Seek VQA Certification in the Canadian Wine Industry
Bibliographic record
Abstract
Abstract The establishment of quality assurance systems is an important development in the wine sector, particularly so for new and emerging wine regions. Focusing on the Canadian wine industry, this article examines the determinants of a winery's decision to adopt Vintners Quality Alliance (VQA) certification for wines. The analysis also examines whether wineries seek VQA certification for higher-priced wines or whether VQA certification leads to higher wine prices. To examine the certification decision, a probit model is applied to a detailed data set of Canadian wines sold in Ontario over the period 2007–2012. Wines from wineries that supply large volumes of wines (more than 1,000 cases) are more likely to have VQA certification, as well as ice wines and wines from specific regions. A Hausman specification test for endogeneity suggests that VQA certification leads to higher wine prices and not the other way around. (JEL Classifications: D22, L15, L66, Q13)
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".