Cultivating common ground: the story of food (and the food in stories)
Bibliographic record
Abstract
Demystifying the story of food – from seed to store to stomach and how that cycle perpetuates – is a core tenet of food literacy and the central aim of this project. While exposure to environmental issues is critical to developing awareness, young learners are often burdened with crisis-laden facts about the state of our world and our food systems. Approaching difficult subjects using a narrative approach is one way to mitigate this burden. In this project, children’s literature that centres on farms and food production/food gathering in settler and Indigenous contexts is used as a launching pad for discussions about food security. Food is an enduring theme in children’s and young adult literature, and is particularly prevalent in narratives from the past, where food gathering and production are often rooted in their environmental contexts. These food narratives provide a pathway for young readers to critically investigate contemporary environmental concerns from a safe space. This project investigates how children’s literature can be used as part of a critical food pedagogy to enhance the food literacy of young learners and encourage them to find common ground between the physical world and the worlds they read. In locating, analyzing, and experiencing food environments in literature via an affective, indirect approach, food literacy - which is foundational to the development of environmentally responsible behaviour – is enhanced.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.022 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".