Unwrapping school lunch
Bibliographic record
Abstract
Students are important stakeholders in school food programs. Yet children’s daily experiences and voices are often overlooked in advocacy around school food. In Canada, where the federal government recently expressed interest in creating a National School Food Program, nearly no research has documented the first-hand experiences of children during lunch. This ethnographic study draws on data collected during 36 lunchtimes in three Canadian schools during a transitional period in a school district’s lunch program. The findings unwrap the powerful role of students’ perceptions of and relationships to food in shaping their social interactions, and their sense of care, connection, and identity. Classroom observations coupled with photos of school lunches demonstrate the wide diversity of foods eaten at school and the nuanced, complex, and sometimes divergent meanings children give to food, school lunch and the people involved in preparing, serving, supervising, and sharing lunchtime experiences. Students demonstrated in-depth knowledge of the food choices and attitudes of their peers and actively marked out their identities vis-à-vis food. Students frequently talked about food as a site of care and support, and both the social relationships and care work that played out were a major part of school lunch experiences. Understanding the intricacies of children’s school lunch experiences, including the relationships, meanings, and values that shape school lunch, will be critical for creating robust school food programs and policies in Canada that better serve the needs of children and reduce rather than reproduce existing health and social inequalities.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".