Comparative Assessment of Women Involvement in Farming and Family Life in Rural Parts of Nigeria
Bibliographic record
Abstract
The study was carried out through the use of structured questionnaire administered on women in two agricultural zones of Oyo State. The main objective of this paper is to examine the extent of women’s participation in farming specifically, the paper is designed to identify the sources of income and compare income levels in two geographical settings, identify and compare farming activities in two different farming zones. In the paper attempt has been made to characterize the farm labour force, access to land and other inputs in the two different farming zones and on the basis of all these some recommendations that if implemented would lead to increase in output, increase in return from farm and consequently increase in their various contributions to the household have been proposed.Some fifty women were interviewed from the two selected zones. Descriptive statistics was used to explain the background information, socio economic activities farming activities, development projects, as well as the problems encountered by women in the two areas.Multiple regression analysis was used to show the extent to which variables such as age, hectarage, educational attainment and experience in farming affect the income of rural women in the study areas. Dummy variables were added to distinguish between the two areas. This is to show if there was a significant difference in the income levels of the rural women in the areas.The study revealed that the size of the farmland cultivated by women in the two areas was generally small. About 50 percent of the respondents cultivated one hectare of land or below. Low credit facility for farm work and lack of modern inputs like fertilizer and improved seeds were major constraints for increased productivity among the women respondents. Innovations introduced in the study areas include Adult education, improved seeds coupled with improved production package. The t-test revealed the lead equation being semi-log, the coefficients of which are hectarage cultivated, educational level and income from other sources were significant at 95 percent confidence level. About, 59 percent of the variations in the income of rural women interviewed were explained by the independent variables.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".