Potential for sustainability eco‐labeling in Ontario's wine industry
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it