Wild and Indigenous Foods (WIF) and Urban Food Security in Northern Namibia
Bibliographic record
Abstract
Abstract Rapid urbanisation and food system transformation in Africa have been accompanied by growing food insecurity, reduced dietary diversity, and an epidemic of non-communicable disease. While the contribution of wild and indigenous foods (WIF) to the quality of rural household diets has been the subject of longstanding attention, research on their consumption and role among urban households is more recent. This paper provides a case study of the consumption of WIF in the urban corridor of northern Namibia with close ties to the surrounding rural agricultural areas. The research methodology involved a representative household food security survey of 851 urban households using tablets and ODK Collect. The key methods for data analysis included descriptive statistics and ordinal logistic regression. The main findings of the analysis included the fact that WIFs are consumed by most households, but with markedly different frequencies. Frequent consumers of WIF are most likely to be female-centred households, in the lowest income quintiles, and with the highest lived poverty. Frequent consumption is not related to food security, but is higher in households with low dietary diversity. Infrequent or occasional consumers tend to be higher-income households with low lived poverty and higher levels of food security. We conclude that frequent consumers use WIF to diversify their diets and that occasional consumers eat WIF more for reasons of cultural preference and taste than necessity. Recommendations for future research include the nature of the supply chains that bring WIF to urban consumers, intra-household consumption of WIF, and in-depth interviews about the reasons for household consumption of WIF and preferences for certain types of wild food.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".