Food Swamps and Poor Dietary Diversity: Longwave Development Implications in Southern African Cities
Bibliographic record
Abstract
While the literature on food deserts focuses on limited availability of food in urban settings, ‘food swamps’ may better characterize the extensive prevalence and accessibility of cheap, highly processed foods. For urban populations, access to nutritionally inadequate poor-quality food has dire developmental consequences. The long-wave impacts of malnutrition at gestational and early childhood stages are negative and can be non-reversible. Moreover, those who survive into adulthood may face a lifetime of sub-optimal physical and mental development that undermines the second and third UN Sustainable Development Goals—to end hunger and to ensure healthy lives. This paper assesses the long-term health vulnerability of children with limited access to adequate and nutritious food in rapidly urbanizing cities. The analysis focuses on the African Urban Food Security Network (AFSUN) data drawn from 6453 household surveys in 11 cities and nine countries in Southern Africa. The results indicate that children in these households are consuming a limited diversity of food, have limited access to resources and have greater odds of experiencing both short-term and long-term food and nutrition insecurity. These findings demonstrate an underlying vulnerability to long-term health impacts stemming from nutritionally inadequate diets, with potentially significant costs to human capital.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".