Implications on Unemployment and Nigerian GDP
Bibliographic record
Abstract
ICT is defined as any technology that facilitates communication and assists in capturing, processing and transmitting information electronically. This paper considers ICT to be veritable tools to tackle the rising unemployment in Nigeria. Being without a job is indeed an enforced “idleness ” of wage earners who are able and enthusiastic to work but cannot find jobs. ICT can generate youth employment. The increase in mobile phones has led to job creation. Telecentres are being set up in places like shops, schools, community centres, police stations and clinics. The population of Nigeria, according to the National Population Commission (NPC) figures stands at over 140, 000,000. 60 % of this number is made up of youths and many of them just idle away their time with nothing to do. With the institutions of learning in Nigeria churning out graduates of various levels and degrees on a yearly basis, a rising trend has seen these graduates coming out of the nation’s universities and polytechnics to join those who graduated ahead of them but without any means of livelihood for years. This paper examines the role played by unemployment on the making of the Nigerian Gross Domestic Product (GDP) for a period of nine years
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".