Exploring food choices within the context of nutritional security in Gaborone, Botswana
Bibliographic record
Abstract
Food consumption experiences remain largely unexplored in urban Africa, despite mounting concerns regarding both over‐ and undernutrition of city dwellers and the potential impact on overall human health and development. This paper seeks to explore the foodstuffs people consume and the factors that shape consumer choice in Gaborone, Botswana. Empirical data were drawn from food diaries and observations of 40 households and discussions with them, plus key informants interviews in Gaborone. Analysis reveals the range of foodstuff people consume, highlighting the prevalence of diets comprised of energy dense, processed and animal‐sourced foods, which are major nutritional security concerns. However these diets were not summarily western or westernizing as per the nutritional transition thesis, as our analysis suggests subtleties of dietary patterns, including the fact that meat‐based diets are traditionally rooted rather than imported and meals tend to comprise both local and western components. The paper also identifies multiple interacting factors influencing consumer food choices, illustrating how food decisions embody context‐specific personal and social circumstances. Understanding how these factors shape what people eat in Gaborone may enable policy makers to facilitate the conditions within which healthy food choices can be made and to address emerging public health and nutrition challenges in African cities.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".