Domestic Foodwork in Value and Practice: A Study of Food, Inequality and Health in Family Life
Bibliographic record
Abstract
This dissertation explores home-cooked family meals – the ideals and expectations around them, as well as how they are navigated by parents in diverse social positions. This exploration assesses how discourses and practices surrounding family foodwork reflect and shape inequalities in a variety of realms including gendered labour, economic disparities, health outcomes and consumer politics. It utilizes diverse methods including a discourse and content analysis of North American news media, as well as qualitative interviews, cooking observations and food recall conversations with parents in the Greater Toronto Area who are primary cooks in their families. These varied methods facilitate investigation into how home cooking is publicly presented, automatically understood, and emotionally experienced by parents from diverse backgrounds. The dissertation explores these ends in three analytically distinct chapters, offering three key insights. First, the media analysis reveals that public discourse promotes a complex allocation of responsibility for family meals that recognizes multiple structural conditions constraining meals (such as unhealthy food environments and inflated normative standards), yet assigns responsibility for resolving them to individuals (i.e. parents should work harder to combat these constraints and cook more at home). These findings apply to family meals but can also be extended to consider responsibility for social problems within neoliberalism more broadly. Second, interview analysis identifies the ubiquity of a cultural schema of “cooking by our mother’s side”: an automatic, semi-conscious understanding of learning to cook that privileges culinary knowledge acquired during childhood through the social reproductive work of mothers. Analysis of this schema reveals its role in reproducing gendered inequalities and obscuring diversity in food learning, especially by overemphasizing the importance of childhood and masking learning later in life. Third, I qualitatively analyze how socio-economic disadvantage (alongside its intersections with gender and race/ethnicity) negatively impacts the emotional experience of foodwork but does not necessarily predict cooking pleasure. In identifying and exploring five conditions of cooking pleasure, I examine how certain conditions can operate relatively independently from class and facilitate cooking enjoyment for low-income groups. Collectively, the dissertation advances scholarly understanding of the ideals, meanings and emotions encompassing family foodwork, their embeddedness with social inequalities, as well as opportunities for resistance and social change.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".