Drinking Blue: Uncovering the Environmental Impacts of Quebec’s Local Wine Industry
Bibliographic record
Abstract
"Quebec’s nascent local wine industry is growing at an increasingly rapid pace. Among the reasons that account for its popularity, Quebec local wine is commonly presented as an environmentally friendly alternative to market dominant non-local wines. Previous studies, however, have debated the environmental benefits of local production. Within the local food literature, skeptics have argued that the inefficiencies of local food production offset the environmental benefits of proximity. Within the wine industry literature, quantitative studies have shown production and distribution inefficiencies can significantly increase carbon emissions. These questions are of particular concern to urban and regional planners, as a shift towards local food production would lead to considerable changes in urban and rural land uses. Using a qualitative analysis of stakeholders in Quebec’s local wine industry, this study compares stakeholder knowledge with relevant studies on local food and local wine. My findings indicate that Quebec local wine industry stakeholders are biased towards the positive in their views on the environmental impacts of local wine. They also demonstrate that the expansion of local wine in Quebec is linked to the exurbanization of the province’s wine regions. I conclude that, contrary to existing beliefs, local wine production in Quebec is more environmentally damaging than non-local wine production, due to specific distribution and production inefficiencies in the province, as well as links to exurbanization "@eng
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".