How the Focus on Food Literacy in Ontario’s Food Charter Toolkits Detracts from Meaningful Food (In)Security Action
Bibliographic record
Abstract
This article examines the uneasy relationship between the values of food security and food literacy in the context of Ontario’s local food charter discourse. It extends prior research on the competing values and incongruous community identities that food charters constitute by exploring the emerging genre of the food charter toolkit which is intended to help community members implement food charter visions. Situating our analysis within a critical review of recent work on food literacy and its association with food (in)security, we argue that toolkits articulate a superficial and ineffective approach to achieving the food charter goal of “food security for all” because they recommend mainly food literacy initiatives as the primary means for building food-secure communities. Addressed to a privileged audience of citizen-consumers who possess the socio-economic capacity to engage in the toolkits’ recommended actions at both personal and community levels, this genre problematically excludes food-insecure community members as an agentic audience. Despite the ostensible social equity goal to ensure food security for all community members, the prevalence of a neoliberal-communitarian food literacy discourse in the food charter toolkits obscures the systemic-economic causes of, and possible solutions to, food insecurity. pdf
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.024 | 0.025 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".