Economic Empowerment for Rural Women in Nigeria: Poverty Alleviation through Agriculture
Bibliographic record
Abstract
Generally, rural women are majorly involved in agricultural activities such as planting, weeding, harvesting, processing and marketing. They also keep some domestic animals and birds. Empowering rural women will go a long way to improving the economic life of the women and also the well-being of individuals, families and the rural communities. Social, cultural traditions and agricultural constraints can limit rural women’s economic status. Rural women’s limited access to productive resources, low educational level and illiteracy are contributors to rural women’s poverty. Illiteracy affects their chances to benefit from newer, non-traditional methods such as: information and communication technologies. The promotion of agricultural development should be through the provision of useful and relevant information to the farming communities by the extension services. The unpaid work that women perform at home and farm are not recognized for official record. There are many constraints making rural women farmers to be lagging behind economically, apart from lack of agricultural information. The main constraints are the lack of personal land and credit. There is evidence that empowering women in multiple ways will contribute to their own food security and nutrition and that of their families. Women are limited in terms of their potential in contributing to agricultural development. Reducing the gender disparities or discrimination will generate significant gains for the agricultural sector. There is the need for national laws and policies that promote women’s rights to own land, property and have equal access to credit for their businesses.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".