Utilization of Adult and Non-Formal Education Programs in Combating Rural Poverty in Nigeria
Bibliographic record
Abstract
The purpose of this paper was to examine the concept of poverty and its causes in Nigeria and to analyze how adult and non-formal education programs can be utilized to reduce rural poverty in Nigeria. In spite of Nigeria's affluence in human and material resources, it is classified among countries with high level of poverty. Incidentally, the incidence of poverty in Nigeria assumes wider dimensions in rural areas where larger proportion of the population (about 70%) reside mainly as subsistence farmers, fishermen and women, retail traders, herdsmen, palm wine tappers, the aged and hunters whose cultural and occupational affiliations do not encourage them to migrate. The incidence of rural poverty in Nigeria is attributed to lack of adequate education/illiteracy, subsistence farming, protracted illness due to inadequate medical care services, lack of employment opportunities, and failure of government to provide basic social amenities. To ameliorate the extent and dimensions of rural poverty in particular, successive governments, over the years, have introduced various economic recovery programs, most of which failed due to official corruption, lack of proper mobilization and top-down nature of such programs/schemes. This paper advocates a revert to the use of adult and non- formal education programs to educate and train the rural poor outside the formal education system on how and what to do to come out of the stronghold of poverty. In this regard, the use of adult basic education and functional literacy, agricultural extension education, women education, health extension education and vocational skills acquisition programs are seriously indicated. It is strongly recommended that for poverty alleviation programs of government to succeed, community members particularly, the poor for whom the programs are meant, should be well mobilized as to make inputs in the planning, execution, monitoring and evaluation of such programs and schemes that concern them.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".