“Sometimes I feel like I’m counting crackers”: The household foodwork of low-income mothers, and how community food initiatives can support them
Bibliographic record
Abstract
For women parenting on low incomes, there is a significant disparity between household foodwork standards and the resources with which to meet them. This study centres on the everyday foodwork experiences of low-income mothers and their engagement with community supports such as community food initiatives (CFIs). It helps address a research gap concerning the relationship between CFI participation and maternal household foodwork. The study employs multiple methods including semi-structured interviews, graphic elicitation and tours of local community food programs. By identifying a range of factors, strategies, and challenges in mothers’ foodwork, the study elucidates some of the contradictory pressures that low-income mothers experience around foodwork. Some of these pressures are associated with meeting individualizing standards around being "good" mothers, "good" consumers and "good" food program participants. Efforts to meet these standards were seen through mothers’ attempts to feed their children healthy and preferred food, exercise agency through market choices, and moderate their demands of community food programs. While more research is required regarding both mothers’ actual participation in CFIs and CFI strategies to support them, the findings suggest that CFIs should incorporate low-income mothers’ subjectivities into food programming.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".