Time-Geographic Project of Household Food Provision: Conceptualization and a Pilot Case Study
Bibliographic record
Abstract
Geographers and health researchers routinely analyze data on food-related behaviors to understand potential relationships between the food environment and diet. Analytical uncertainties arise, however, from discounting the sequential connections and household coordination of various food tasks. This study employed the time-geographic construct of the project, which is defined as a series of goal-oriented activities conducted by one or more individuals, to understand the composition and influencing factors of household food provision. To demonstrate the usefulness of the project concept, this study delineated how food activities were woven into a select couple’s daily life paths with the aid of sequence visualizations, and developed an analytical test case using time-use diaries of coupled adults living in Toronto, Canada. Ten dinner project archetypes were identified with distinct characteristics of activity composition and coordination. The study further explored how the dinner project archetypes were related to geographic food environments and meal consumption. By employing the project concept in research on food environments, the interconnectedness between various diet-related activities and diverse patterns of coordination between household members can be captured. Finally, a discussion on how the project perspective can improve the understanding of food environments and healthy eating was presented.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.005 |
| 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".