<scp>Ultra‐Processed</scp> Food, Depletion, and Social Reproduction: A Conceptual Intervention
Bibliographic record
Abstract
Abstract What we eat and how we think about food and nutrition are undergoing a momentous change, driven by the rise of ultra‐processed food. There is a growing body of evidence linking the consumption of ultra‐processed food to poor health outcomes. However, the health depleting effects of ultra‐processed food go beyond changes in discrete indicators of nutrition and health. Processes of depletion entail social, economic, and political relations. This paper aims to emphasise the importance of a social science research agenda on ultra‐processed food by establishing the conceptual connections between ultra‐processed food and depletion using a social reproduction approach. To do this, it draws on the notion of depletion through social reproduction elaborated by Shirin Rai, Catherine Hoskyns and Dania Thomas, which provided inspiration to unpack the totality of social reproduction and consider specific resources needed for social reproduction. Such an approach reveals that ultra‐processed food is both an input for social reproduction, through consumption, and a form of social reproduction work, when food work and the associated (health) care work are considered. On this basis, the paper identifies four conceptual dimensions to explore whether and to what extent the expansion of ultra‐processed food can cause depletion and the key methodological principles to use this conceptual approach in empirical research.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.031 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".