Lunchbox shaming: recollections of school lunchtime by young Canadians of Asian descent
Bibliographic record
Abstract
Children’s home-packed lunches to school reflect family’s culinary cultures, social locations, and unique food norms. At Canadian elementary schools, children of Asian heritage sometimes experience “lunchbox shaming” – feeling embarrassed for bringing foods that are seen deviant from dominant food norms. This study explored the recollections of school lunchtime by 25 young adults (aged 17–25 years) from three largest Asian ethnocultural groups (Chinese, Indian, and Filipino) in Toronto, Canada. Two parents of the participants also joined follow-up interviews to provide their insights. Our analysis focused on four layers of meanings at micro-level (personal experiences and emotions), meso-level (family food practices, school food environment), macro-level (socio-historical discourses), and interactional-level (researchers’ positionalities). Many participants recalled painful experiences being teased by classmates of their “stinky” lunch, throwing away homemade lunches, or asking parents to pack “normal” lunches to fit in. Conversely, a few shared positive memories of their lunches being praised and felt proud of their culinary heritage. Many reportedly felt a need to balance affiliation to both their home and school food cultures while growing up in Canada. We conclude by discussing the implications of the study findings for the ongoing debate on Canada’s national school food program and food literacy education.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.006 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".