Cooking up an injustice: A critical examination of the role of cooking programs in reducing health inequalities
Bibliographic record
Abstract
The way we eat is one of the biggest causes of preventable illness and death, particularly for those living in more deprived areas. Public health interventions often include courses teaching cooking ‘from scratch’ as an affordable means of dietary improvement, but this paper questions the effectiveness of such programs. Using an in-depth case study of a leading healthy cooking programme, including ethnographic observations of seven cooking classes and interviews with 35 participants and three members of staff, we show that the impact of this programme was limited by its adherence to conventional ‘nutrient-focused’ framings of healthy eating. Teaching based on this framing created confusion by separating nutrients from foods, hampered embodied learning of skills and ultimately failed to address how learnings could be integrated into the everyday lives of participants Unable to engage with inequities in access, preparation time, food environments or other sociocultural influences on eating habits, courses built on similarly nutricentric foundations will never be able to address the major barriers to healthy eating faced by their target participants. As an alternative, we propose that cooking courses grounded in a more ‘practice-based’ understanding of healthy eating would be more effective at changing dietary behaviours, especially in areas of higher deprivation.
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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 | 0.005 | 0.016 |
| 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 | 0.002 |
| 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".