Understanding the factors contributing to food security among under-recognised indigenous vegetable farming households in Nigeria
Why this work is in the frame
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Bibliographic record
Abstract
Integrating under-recognised indigenous vegetables into cropping systems presents a viable strategy for enhancing household food security. Despite their potential, these native crops remain under-researched. This study examined the determinants of food security among households engaged in the cultivation of under-recognised indigenous vegetables in Nigeria. A total of 302 respondents were selected using a simple random sampling technique from the NiCanVeg farmers’ lists. The data were analysed using a Zero-One Inflated Beta (ZOIB) regression model, which is appropriate for handling proportions with a considerable number of zero outcomes – common in food security indicators. The use of the ZOIB model helped correct for the bias introduced by zero responses. The analysis revealed that socio-economic factors – including sex, age, education, household size, savings, association membership, marital status, total income, income derived from under-recognised vegetables, market participation, and asset value – significantly influenced both the probability and intensity of food security. The findings underscore the importance of promoting education and market participation as strategic interventions to improve food security among farming households cultivating under-recognised indigenous vegetables.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it