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Record W6901749366 · doi:10.60692/8eckb-kdx28

All Progressives Congress (APC) N-power Programmes and Unemployed Nigerians in Nigeria

2019· article· en· W6901749366 on OpenAlexaboutno aff

Bibliographic record

VenueGreater South Information System · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicAfrican Education and Politics
Canadian institutionsnot available
Fundersnot available
KeywordsNigeriansGovernment (linguistics)UnemploymentMindsetQuarter (Canadian coin)Curriculum

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.373
Threshold uncertainty score0.551

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.025
GPT teacher head0.276
Teacher spread0.252 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2019
Admission routes1
Has abstractyes

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