Rural Women Farmers and Food Productivity in Nigeria: An Example from Ekiti Kwara, Nigeria
Bibliographic record
Abstract
This paper describes the earning activities of Rural Women Farmers in Ekiti Kwara to identify processes of food production and Agriculture within a farming season.200 farmers were examined through the use of questionnaire administration. Results were processed through the use of simple tabulations and inferential statistics. Findings revealed that that 75.5% of the respondents have access to land for farming while the remaining 24.5% not to have a land of their own ,but they are those that depend on either family lands or go on leasing from other land owners. Apart from their mothering roles, women here are contributing heavily to nutritional development, farm employment and food security. About 54.5% of the women farmers in the study area provides food for their household; topmost in their farm productions are in the area of vegetables, fruits livestock and fishery. However, income earnings from farm is generally low due to the subsistent nature of their production .The result of the stepwise multiple regression showed that about 75.69% differences in the variation of women participation is explained by three variables of supplementing income, debt servicing measure and food security respectively and appropriate recommendations are presented accordingly.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".