How the Food Environment, Time Use, and Household Labour Division Influence Dietary Activities: A Case Study of Two Neighbourhoods in Toronto
Bibliographic record
Abstract
The dietary transition from home-prepared meals to increased reliance on food prepared away from home has led researchers to study the factors influencing participation in various food-related activities. While exposure to food environments, a key factor impacting what food people purchase, has been extensively studied, the dynamic spatiotemporal contexts for food retail exposure remain underexplored. Time use also plays a role in what people eat. However, most studies have neglected the division of household labour, a factor that can help explain the ways coupled partners spend time on food activities. Furthermore, in food environment research, the prevailing reductionist view conceals compositional and contextual complexities of household food-provisioning practice. To address these research gaps, this dissertation aims to develop conceptualizations and methods to better understand how the food environment, time use, and household labour coordination collectively influence dietary behaviours, using a case study of two neighbourhoods in Toronto. Chapter 2 proposes a novel application of multi-channel sequence analysis in food environment research based on time use diaries and GPS trajectories concurrently collected. This method offers an effective way to assess how food exposure in varying spatiotemporal contexts is associated with food activities. Chapter 3 expands the individual-level time use analysis of in-home food chores to the household level by examining time use diaries of coupled men and women. The results suggest possible gender differences in responsiveness to partners’ time allocations, highlighting a need to account for potential impacts of intra-household dynamics on food behaviours. Chapter 4 proposes to conceptualize the interconnected food-provisioning activities undertaken by household members using the time-geographic concept of the project. The applicability of this conceptualization is demonstrated through a test case on coupled adults’ activities for providing dinner. Chapter 5 summarizes this dissertation and discusses how the three chapters above contribute to advancing the current research. Ultimately, these chapters substantially improve our understanding of the various ways in which the food environment, time use, and household labour division influence food behaviours and expand the conceptual and methodological approaches that are geared toward a more holistic understanding of food behaviours in the built environmental and social contexts.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".