Cultivating critical and food justice dimensions of youth food programs:
Bibliographic record
Abstract
In this article we present accounts of two youth food programs operating at a Community Food Centre. One program, Kids Club, engages children, aged 6 to 12, in cooking and gardening activities; the other, Cookin' Up Justice, is directed to adolescents (13 to 18 years) and explores food justice concepts through experiential group cooking. A variety of ethnographic methods including participant-observation, semi-structured interviews, focus group and photovoice discussions done with youth participants and parents are used to document how the food programs incorporate innovative aspects of Critical Food Literacy and Food Justice. We address the successes, challenges, and opportunities in delivering youth food programs that incorporate both the “practical” and “political” dimensions of Food Literacy and Critical Food Literacy with particular attention to food politics that arise when working with racialized, newcomer participants living in a lower socioeconomic neighbourhood. We also discuss the challenges and opportunities in doing food programming with the adolescent demographic. We recommend that community food programs incorporate an analysis of the cultural, racialized, class, and gendered aspects of their staff and participants into the Critical Food Literacy and Food Justice dimensions of their programs to promote anti-racist and inclusive program design and facilitation.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.013 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".