Exploring Chefs’ Behaviours and Attitudes Influence Public Awareness of Sustainable Food Practices
Bibliographic record
Abstract
The dominant food system is having a major impact on our planet causing extreme environmental change from greenhouse gas emissions to deforestation along with inadequate and unhealthy diets for much of the world’s population. Chefs are important actors in food systems, who have the capacity to influence consumer awareness and food choices, and thus have a role to play in helping to shift towards a more sustainable food system. However, existing research on this topic is limited. Drawing upon environment studies literature and primary research, including interviews with chefs working in restaurants and culinary teaching institutions in the Kingston, Ontario area. This study explores how chefs’ behaviors and attitudes can influence sustainable food practices. This study expands on initial research on this topic by providing a community-based research project focused on Eastern Ontario. My findings demonstrate that chefs have the knowledge and skills to make decisions that can impact consumer's choices and practices. Chefs' emotional and symbolic relationships towards food drive their sustainability motives, as this creates community and meaning behind what they do. Thus, when chefs have access to resources on sustainability (environmental, social, and economic impacts of food) and sustainable food practices and techniques, they can be motivated and inspired to contribute to food system change. However, current policies shaping who produces food, how food is produced, and how food waste is managed limit chefs’ abilities to change processes and practices within the food system.
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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 | 0.002 | 0.005 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".