What does ‘buying local’ mean to wine consumers?
Bibliographic record
Abstract
This study sought to understand what buying ‘local’ means to Ontario wine consumers and determine how local wine purchase behaviour varies with select demographic and environmental belief factors. Few studies concerning the perception of and reasons for purchasing local wine have been conducted, and none in the context of Ontario wine consumers. An online survey of Ontario wine consumers (N = 521) was carried out and results showed that perceptions of localness differed between food products (‘coming from within a 100 km radius of home’) and wine (‘coming from anyway in North America and Canada’). The most important motivational factors reported for purchasing local wine were directly linked to economic and hedonic factors, specifically; ‘support local vineyards and wineries’, ‘build the local economy’ and ‘taste and flavour’. High frequency purchasers of local wines also bought local foods more often and were more likely to seek information about the origin of their food than were lower frequency purchasers. A pro-ecological worldview is associated with higher purchasing frequency of Ontario wine. These results can assist Ontario wineries with respect to market segmentation and development of campaigns focused on local wine.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".