Agrifood systems literacy: Insights from two high schools’ programs in Ontario
Bibliographic record
Abstract
Following the increased industrialization and globalization of the prevailing agrifood system, researchers and practitioners have highlighted the detrimental impacts of this model on human health, food security, and the environment. As such, experts and citizens are calling for an increased awareness, through food literacy (FL), to improve health and justice and to transition towards sustainable agrifood systems. Building on field research, critical pedagogy, and existing FL analyses, we argue for incorporating both health and well-being, and agrifood systems dimensions into FL programming. By doing so, FL can contribute to promote individual health, as well as more sustainable agrifood systems policies and practices based on the principles of food sovereignty. Through qualitative research with students and teachers in two Ontario high schools, we explore the content and approaches taken in food-related programming. Aspects of FL among students are also explored in order to highlight their strengths and limitations. Further, we point to the challenges faced by teachers in delivering food-related courses. We propose a conceptual framework that highlights the benefits of including the multiple dimensions of FL as a way to test and improve existing FL programs, and eventually train future generations of teachers, students, and citizens.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.020 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".