Non-formal Education Programmes and Poverty Reduction among Young Adults in Southern Senatorial District, Cross River State, Nigeria
Bibliographic record
Abstract
The study investigated the influence of non-formal education programmes on poverty reduction among youngadults in southern senatorial district of Cross River State, Nigeria between 2000-2005. Three hypotheses werepostulated and tested. Data were collected using the Poverty Reduction Inventory (PRI). The stratified randomsampling technique was used based on location (Local Councils and existing political wards) to select four localgovernment areas and forty local wards. From these local government areas and wards, simple random samplingtechnique was employed to select two local government areas and thirty wards from which three hundred (300)youths were selected as sample for the study. Data collected were analysed using analysis of variance andindependent t-test analysis. Results obtained showed that acquisition of vocational skills lead to a significantreduction of poverty among young adults, and participants’ age on skill acquisition programmes significantly,influenced poverty reduction. Similarly, the results revealed that there is no significant difference in povertyreduction between male and female youths. On the basis of these findings recommendations were made.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".