Exploring Traditional Weaning practices in North Western Nigeria; Food, Knowledge and Culture: A Step towards Safeguarding Community Food Security
Bibliographic record
Abstract
Abstract For many women in Nigeria who practice exclusive breast feeding, the weaning period is a crucial time. This is when children are introduced to solid food and such dietary change can be challenging for them, but also for care givers who are saddled with the responsibility of providing sufficient and nutritious food that would support healthy development. At this period, many women in rural communities utilize traditional foods of various kinds as weaning food. This paper highlights traditional food used by care givers in Makarfi Local Government Area (LGA) during weaning and the cultural teachings attached to weaning practices. Semi-structured interviews were conducted for 60 women who were purposefully selected from the ten districts in Makarfi LGA in Kaduna state, Nigeria because of their status as care givers. Results show that 95% of the participants derive their knowledge about foods used for weaning through cultural teachings that has been passed down through generations, while 5% got information from health practitioners. Some of the traditional foods used for weaning purposes include gyeda (Arachis hypoaea) and gero (Sorghum bicolar). This study reinforces the importance of traditional food and knowledge; and the need to take into consideration cultural practices when making food security policies.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".