Investigating the impact of Skill Acquisition Programmes on Graduates Unemployment Dilemma in Lagos State, Nigeria
Bibliographic record
Abstract
Graduates Unemployment (GU) in Nigeria has hit the roof. Studies have established that graduate unemployment has increased from 12.1 per cent in the first quarter of 2016 to 13.3 per cent at the end of the second quarter in the same year. Lagos state being the centre of attraction and the most popular city in Nigeria has the highest percentage of unemployed graduates. Therefore, the study examines the impact of skill acquisition programmes on graduate unemployment rate in Lagos State, Nigeria. The Refugee effect and Schumpeter effect on unemployment and entrepreneurship was employed as theoretical guide. Descriptive survey designed was adopted for the study. Sample size comprised of 326 students of 4 Skill Acquisition Centres (SACs) in selected Local Government Areas of Lagos State, Nigeria. The selection of the respondents was done through multi-stage sampling technique. Semi-structured questionnaire was used for data collection. Data were analyzed using descriptive and inferential statistics. The finding revealed that skill acquisition programmes have direct influence on graduate unemployment reduction in Lagos State, Nigeria. It also promotes culture of creative ideas, self-reliance, business initiative as well as low level of dependency among the youths in Lagos State, Nigeria. Thus, it was recommended that more efforts in terms of funds, facilities and materials must be provided by the government and non-governmental organizations to expand the capacity of skill acquisition programmes in Nigeria in order to help actualize the dream and vision of a better life for the youths. Keywords : graduate unemployment; skill acquisition programmes; youths; Lagos
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".