High Poverty Rate Amidst Empowerment Programmes: The Impact of Skills Acquisition on Poverty Reduction in Rural Cross River State, Nigeria
Bibliographic record
Abstract
This study sought to examine skills acquisition and its effect on poverty reduction in rural Cross River State, Nigeria. Thus, providing policy guide towards government effective implementation of skills acquisition programmes in the state in order to reduce poverty at the grassroots. Survey method of research was utilized to obtain information from households and communities. Five thousand and four (5004) respondents were selected using ballot method. Data were analysed using Pearson Product Moment Correlation (r x y). The findings of the study show that the skill acquisition programmes organized by Cross River State Government has not significantly reduced rural poverty in the state. The study concluded that despite government intention to provide skills to the rural people in order to be functionally relevant in their local environments, there were some obnoxious practices which served as obstacles to the successful implementation of these programmes in the rural areas of the state. Some of these include: the supply of obsolete equipment, dishonest attitude of the programme managers and prebendal selection of beneficiaries. Based on the findings, the study recommended that the government should increase the quantity of items and materials given to the trainees. One cannot acquire skills without equipment. It amounts to giving somebody training without creating job opportunities. It is as good as nothing. Therefore, if government is serious with poverty reduction programme, trainees should have their materials ready at the point of graduation or passing out. Capacity building demands that availability of materials as the basis for success on a given task should be given prompt attention.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".