Bibliographic record
Abstract
The ‘food environment’ of a city can be defined as the location and type of food sources found in an urban area, as well as the broader environmental factors that affect the production, retail and consumption of food in the city. The food environment of cities has an enormous impact on food security and on the health and wellbeing of residents, but this relationship has been under-recognised and under-studied, particularly in the global south. Drawing on work undertaken as part of an ESRC-funded project, Consuming Urban Poverty, on governing food systems to alleviate poverty in secondary cities in Africa, as well as other work undertaken by the African Centre for Cities, this chapter explores the multi-faceted ways in which the food environment of cities can impact on human health and wellbeing. First, the chapter examines the food environments of African cities, with a focus on the built environment, highlighting the diverse range of food outlets and complex patterns of food access. Second, it explores the multi-faceted ways in which the food environment of cities can affect human health and wellbeing. Finally, the chapter discusses possibilities for how food environments that are more conducive to health and wellbeing can be created and sustained.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".