All Progressives Congress (APC) N-power Programmes and Unemployed Nigerians in Nigeria
Bibliographic record
Abstract
This study examined the N-Power programmes and Unemployed Nigerians under the All Progressives Congress (APC) led federal government.The year 2016, was the actual year the programme started in Nigeria to make an impact on the lives of unemployed ones.The N-Power programmes are into three parts: N-Power Volunteer Corps (N-teach), N-Power Knowledge and N-Power Build.From our study, the programme has immensely gotten some numbers of unemployed Nigerians more engaged, more productive and more resourceful on duty.Looking at the Nigerian bureau of statistics 2018 data showed a 23.1% increase in unemployment for the third quarter of 2018 which brought the whole figure of the unemployed in the country to over 21 million Nigerians.In addressing this problem, the federal government in their own wisdom initiated N-Power as one of the social intervention programmes in the country.In addition to that, we suggest to the federal government to ensure that curriculum in schools are restructured in a way that two years are used in entrepreneurial studies in a four years duration course in the university and by so doing students shall not have the mindset of looking for job after school but rather having the mindset of creating jobs for themselves and others.And the federal government need to ensure that students are taught how to solve ''social problems'' not just to pass exams and come out with good grades!The very rich ones in the world are so rich because of knowledge economy and innovations.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".