Implementation of operational environmental practices in the Ontario (Canada) wine industry : perceptions, constructs, intent
Bibliographic record
Abstract
Environmental sustainability is a topic of great interest in the Ontario (Canada) wine industry. Following the lead of several wine industries around the world, the Wine Council of Ontario launched a proactive plan for environmental sustainability that culminated with the release of the Environmental Charter for Winemaking Industry in 2007. The Charter outlines environmental best practices and establishes benchmarks for the grape and wine producers in Ontario. With some wineries pioneering the implementation of the recommended environmental practices and others taking a backseat and delaying it, this study’s purpose is to understand the intent to implement environmental practices as part of operational processes within the Ontario (Canada) wine industry by using the Theory of Planned Behaviour (TPB) as the framework of analysis. A constructivist approach using multiple case study design is used to explore the determinants of intention. Twenty wineries are interviewed and repertory grid employed as the chosen technique of data collection. Cluster, content and principal component analysis are conducted with the results indicating that TPB is an appropriate frame of analysis for implementation intent. Using a multidisciplinary approach, this study proposes an updated model for intention applicable to environmental practices. As a practical contribution, recommendations and a list of motivators of implementation intent is developed. Further research to test the proposed model is suggested to alleviate case studies limitations.
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.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".