Shaping sustainable consumption: Quebec consumers' knowledge and beliefs about the environmental impacts of food
Bibliographic record
Abstract
There is growing evidence that shifts in food consumption have the potential to mitigate the environmental impacts of food systems. Yet, although Canadians are increasingly concerned about climate change, changes towards more sustainable food consumption patterns are not widely observed. Understanding consumers' perspective on these issues is crucial for bridging this gap between individual behaviors and collective concerns. This study explores the knowledge, understanding and beliefs of Quebec consumers regarding the environmental impacts of food and their potential for shaping sustainable food consumption. A representative sample of consumers (N = 978) answered an online questionnaire assessing their factual knowledge and investigating their views of food systems impacts. Results indicate low levels of knowledge and highlight widely shared beliefs regarding food systems. Consumers tended to overestimate the role of transport in food's environmental footprint and believe that reducing transport or eating local foods are the most effective ways to improve environmental sustainability. Likewise, consumers tend to underestimate the impact of production, as well as solutions that could effectively reduce this impact. The findings reveal a need for improved literacy and awareness of the environmental impacts of food, thereby highlighting the importance of effective information and education strategies to shape sustainable food consumption habits.
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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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".