Bibliographic record
Abstract
While Canadian policy makers are considering expanding school food programs in Canada, parents remain primarily responsible for packing lunches. Although women perform disproportionate amounts of foodwork, including feeding their children on school days, little research has investigated mothers’ experiences of packing school lunches in Canada. Drawing on 14 interviews with mothers of elementary-aged children in British Columbia, this study explored how mothers experience and make meaning of packing school lunches. Mothers described lunch packing largely as an individualized responsibility for children’s nutritional health and general wellbeing. Mothers strived to enact largely unattainable ideals about packing a “good” school lunch and engaged in diverse forms of physical, mental, and emotional labour to do so. When mothers were perceived to fall short of elusive lunch packing ideals, mothers judged themselves and other mothers, and also reported feeling scrutinized by other parents, teachers, and their children. While assuming the bulk of labour related to school lunch work, mothers also forged connections with their children through lunch packing, which they viewed as emotionally meaningful and a symbol of their care, love, and parental responsibility. These findings show that mothers’ experiences with lunch packing are complex and wrapped up in notions of “good” mothering and feeding ideals. For mothers, a “balanced” lunch requires not only a nutritionally adequate meal, but also involves balancing various forms of labour and contradictory emotions about food work. Understanding mothers’ experiences of lunch packing is pivotal for successfully developing school food programs that meet the complex expectations of Canadian families.
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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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.020 | 0.024 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.033 | 0.005 |
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".