Potential for sustainability eco‐labeling in Ontario's wine industry
Bibliographic record
Abstract
Purpose The purpose of this paper is to explore the degree of consumer interest in an eco‐labeling program for the Ontario wine industry and determine whether there is a willingness‐to‐pay a premium for eco‐labeled Ontario wines. Design/methodology/approach The study was a quantitative survey of 401 wine consumers in Ontario, collected at Liquor Control Board of Ontario (LCBO) retail stores and winery retail stores. Results were analyzed using quantitative non‐parametric statistical analyses. Findings It was revealed that while most Ontario wine consumers do not presently purchase eco‐labeled wine regularly, the majority (90 per cent) are at least somewhat interested in purchasing eco‐labeled wine and that the majority would be willing to pay a premium of $0.51 or more (65 per cent). Consumers also indicated a preference for a seal of approval style label with multiple levels that contained a website from which they could obtain detailed information on certification. Practical implications These results provide valuable insights into wine consumers' purchasing behaviours and purchasing preferences with regards to environmentally friendly products. This information can be useful to those involved in implementing the Ontario wine industry's sustainability initiative, Sustainable Winemaking Ontario (SWO), and to wineries and winegrowers who are interested in promoting their actions taken to improve sustainability. Originality/value There is presently no published research investigating the potential role for an eco‐labeling and certification program for the Ontario wine industry, or any other Canadian wine industry. There is also a limited research on willingness‐to‐pay within the food and beverage sector.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".