Demystifying the Contribution of African Indigenous Vegetables to Nutrition-Sensitive Value Chains in Kenya
Bibliographic record
Abstract
African Indigenous Vegetables (AIVs) are widely consumed in Kenya as part of everyday meals. They provide the much-needed micro-nutrients which are critical for combating micronutrient deficiencies (“hidden hunger”). The study describes the socio-economic characterizes of households in rural and peri-urban areas in Kenya and appraises the contribution of AIVs to household food access. The results show that there are spatial variations in the consumption of AIVs. Households living in rural areas have a wider variety of vegetables and consume their own production for an estimated ten months in a year; at the same time, purchase vegetables for between 6-7 months. Their peri-urban counterparts have less variety, consume their own produce for 11 months in the year and purchase for 8-9 months. Household income plays a critical role in enabling participation in food markets, Households living in rural areas earn significantly less on average from their land, their annual salary and net profits compared to their peri-urban colleagues. At least 40 per cent of households living in rural areas compared to an estimated 20 per cent in peri-urban areas grade their vegetables. In contrast, 50 per cent of all households wash their vegetables before consumption. In conclusion, households’ living in rural areas are net buyers of food, indicating that interventions to ensure increased consumption of AIVs must be accompanied by broad-based livelihood improvements to ensure that benefits accrue. Also, there is a need to underscore the importance of extension services as knowledge brokers.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".