Food and eating as social practice – understanding eating patterns as social phenomena and implications for public health
Bibliographic record
Abstract
Globally, public health agencies recognise obesity trends among populations as a priority. Explanations for population obesity patterns are linked to obesogenic environments and societal trends which encourage patterns of overeating and little physical activity. However, obesity prevention and nutrition intervention focus predominantly on changing individual level eating behaviours. Disappointingly, behaviour-based nutrition education approaches to changing population eating patterns have met with limited success. Sociological perspectives propose that underlying social relations can help explain collective food and eating patterns, and suggest an analysis of the sociocultural context for understanding population eating patterns. We propose a theoretical framework for the examination of eating patterns as social phenomena. Giddens' structuration theory, in particular his concept of social practices understood as an interplay of 'agency' and 'social structure' (rules and resources), is used to study food choice patterns. We discuss the application of these concepts for understanding routine food choice practices of families, elaborating how rules and resources configure the enabling or constraining conditions under which actors make food choices. The framework assists in characterising how social structural properties are integral to food choice practices, and could direct attention to these when considering nutrition interventions aimed at changing population eating patterns.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".