Doom, gloom, or boom? Perceptions of climate change among Canadian winegrowers
Bibliographic record
Abstract
Emilie Jobin-Poirier,1 Gary Pickering,1–4 Ryan Plummer1,4,5 1Environmental Sustainability Research Centre, Brock University, St. Catharines, Ontario, Canada; 2Department of Biological Sciences and Psychology, Brock University, St. Catharines, Ontario, Canada; 3Cool Climate Oenology and Viticulture Institute, Brock University, St. Catharines, Ontario, Canada; 4Sustainability Research Centre, The University of the Sunshine Coast, Sippy Downs, Queensland, Australia; 5Stockholm Resilience Centre, Stockholm University, Stockholm, Sweden Background: Climate change (CC) could have both positive and negative consequences for the Canadian and global wine industries. Understanding how winegrowers perceive CC, however, can provide insight into how to better assist the industry to cope with the impacts of a changing climate. Material and methods: An online survey of 122 Canadian winegrowers was conducted to understand knowledge, beliefs, environmental values, and perceptions towards CC and its impact on the Canadian wine industry. Environmental values (New Environmental Paradigm score), subjective and objective CC knowledge, CC skepticism and uncertainty, belief in anthropogenic CC, and perceptions of the impacts of CC were measured using established tools. Results: Overall, results show that Canadian winegrowers have a relatively low level of CC skepticism, a medium level of CC scientific knowledge, a pro-ecological (as opposed to anthropological) worldview, and generally believe that CC is caused by a mix of anthropogenic and natural forces. Moreover, a majority of respondents (60%) believe that CC has both positive and negative consequences on their vineyard and winery operations, while 8% think that climate change has no consequence on their operations. An extended growing season for grapes, the improvement of grape and wine quality, and the possibility to grow varieties that are not currently viable were the main beneficial consequences of CC reported by participants, while an increase in both disease and pests in the vineyard were the most commonly identified disadvantages. Finally, no association was observed between CC skepticism, knowledge, environmental values, and the perception of CC consequences. Conclusion: Our findings inform communication strategies for the wine industry around CC, and provide important baseline information on winegrowers’ perceptions that inform wider efforts to improve the capacity of the industry to develop and adapt to the consequences of CC. Keywords: wine, grapes, sustainability, adaptation
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".