Beyond “An Apple A Day”: Advancing Education for Critical Food Literacy in Ontario’s School System
Bibliographic record
Abstract
Food is in many ways a connective tissue of the human experience. Over the course of the last century, changes to both local and global food systems has “distanced” eaters from the sources and impacts of the food we eat and the political and ecological systems it is a part of. \nThe agroindustrial food system has produced a wide range of crises, including impacts to the degradation of land, soil, species and water and climate, human health, culture, farmer livelihoods, food and agroecological knowledges, and citizenship. Many scholars have written about food and environmental crises as being reflections of a “crisis in education”. Numerous forms of food education, prolific in recent years, have emerged as a response to the idea that \npopulations require more knowledge in order to “better” engage with the increasingly complex nature of food and food systems. Food education is understood as a conduit for increasing “food literacy”, which in turn is assumed to be part of the “solution” to problems caused by the industrial food system. However, expressions of food education ranging from corporate food marketing of ‘healthy’ and ‘ethical’ foods, public health campaigns which teach the individual to eat ‘better’, to notforprofit programming focused on food justice and active engagement carry disparate drivers and goals, shaped by the discourses most relevant to their locations. This has contributed to an international phenomenon where normative statements are made, largely in siloed environments (Martin, 2018), about what it means to be “food literate”. \nThe discipline of social determinants of health has illuminated how people’s choices, behaviours, attitudes and pathways to positive health outcomes are constrained and shaped by structural and institutional factors which aren’t equitably distributed among human populations. \nIt follows that food literacy frameworks should move beyond education which “treats” the individual, towards education which “treats” the very structural roots that make food literacy necessary. \nAs food literacy becomes a more prominent feature of Ontario policy and subsequently shapes schoolbased learning, it’s important that we ask, What kind of food literacy do we want Ontario students to graduate with? The kind that reinforces existing crises?, Or, the kind that presents the possibility for change? \nThe main goals of this paper are to build upon and contribute to the literature which engages with the intersection of food, environmental education, and critical literacy, to broaden popular conceptualizations of food literacy by bringing to the fore frameworks which address the root causes of food system dysfunction, to present possibilities for a food education practice that relocates the discursive space for determining “what counts as food literacy” (Kimura, 2010, p.466), and to consider how these things can respond to the increasing calls for food education to be advanced in Ontario schools. \nDrawing upon existing literature, education policy review, as well as qualitative data obtained through interviews with 12 people who work as teachers, formal and nonformal facilitators, and academic researchers from public schools, external organizations and universities, predominantly based in Ontario, this paper will explore the processes that would allow for critical food literacy to become an integral component of Ontario’s public education system. This goal of this paper is not to provide fixed solutions, but rather to help develop our collective understandings of what it means to nourish ourselves, our world, and each other.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.050 | 0.022 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".