Giving a Fork about the Environment: Discursive Articulations of Food, Climate Change, and Environmental Sustainability in Canada's Food Guide
Bibliographic record
Abstract
This research explores how the Canadian federal government incorporates climate change and environmental sustainability concerns in the 2019 iteration of Canada's Food Guide and its supporting documents.Using a mixed analytical approach to discourse analysis, I analyze 52 government documents to discover how food, climate change, and environmental sustainability are discursively linked.My findings reveal that these considerations are wed together through dominant storylines that operate as channels to enact change; positioning citizens to adjust their behaviours to be more environmentally benign as a 'solution'.I argue that the guide's 'solutionist' approach to communication constructs a 'good' Canadian consumer and neglects larger questions over creating enabling environments.In doing so, I contend that the 'solutionist' approach acts as a cornerstone for transforming food guides to address climate change and sustainability at the individual level but does not sufficiently address the need for systemic change.vi 5.3.2Revisiting Approaches to Systems Transition .......
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.028 | 0.030 |
| Scholarly communication | 0.015 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".