Understanding Canadian Winegrowers’ Perceptions of Climate Change and Their Implications for Adaptation Behaviors
Bibliographic record
Abstract
Climate change (CC) is currently impacting and will continue to affect the international and the Canadian wine industry in the future. Understanding how Canadian winegrowers perceive CC and address its consequences through adaptation can help support the grape and wine industry in the context of CC. The thesis aimed to understand how winegrowers perceive CC and the ways CC adaptation is occurring throughout Canada. Two studies were conducted in the provinces of Ontario, British Columbia, Québec and Nova Scotia. The first study of this thesis characterizes winegrowers with respect to their environmental values, CC knowledge and beliefs, and their perception of the consequences of CC on their winegrowing operations. The second study describes the present state of CC adaptation in the Canadian wine industry, as well as the adaptation strategies currently used and considered for future implementation to cope with specific weather events associated with CC. This study also investigates the attributes that drive CC adaptation throughout the country. Together, the two studies provide an overview of CC perception and adaptation in the main winegrowing provinces of Canada for the first time in literature. The thesis also contributes to the scholarly literature on CC perception and adaptation by highlighting the drivers that influence winegrowers’ adoption – or lack thereof – of adaptation practices in their operations. It also offers practical information that can be used by stakeholders of the industry to communicate CC information and adopt new practices to address its effects.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".