Standard North American food work : unveiling the invisibilized labour of low-income families through food bank work
Bibliographic record
Abstract
Social policy and public discourse about family life are often rooted in the Standard North American Family (SNAF) ideal, which envisions a heterosexual, nuclear family with a breadwinning father and a homemaking mother, thus obscuring and marginalizing diverse family structures (Griffith and Smith, 1987; Smith, 1993). Charitable food assistance programs (CFAPs), such as food banks, operate under this SNAF assumption, overlooking the emotional and logistical burdens placed on marginalised families that fall outside of SNAF. This dissertation, grounded in in-depth interviews with 31 low-income parents in Canada and employing grounded theory and institutional ethnography, introduces the concept of the “Standard North American Food Work” (SNA-Foodwork), which extends Smith’s SNAF concept to include class-based and racially influenced expectations around food-related tasks in the household. It also introduces “food bank work” (FBW), highlighting the cognitive, relational, and emotional (CRE) work in three distinct stages: planning schedules and transportation, navigating the on-site experience, and managing, sorting, and preparing the acquired food. This dissertation contributes to the scholarly conversation on "work" by broadening the understanding of foodwork and how the SNAF ideological code marginalizes non-traditional family structures. By focusing on the lived experiences of low-income, immigrant, and single-parent families, the study reveals how the SNAF model fails to recognize the extensive work involved in securing food and managing households in challenging economic and social conditions.
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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 | 0.030 | 0.023 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| 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".