Governance and Youth Unemployment in Nigeria
Bibliographic record
Abstract
Numerous studies have observed that governance matters in economic growth and subsequently employment generation. Despite the overwhelming evidences on the importance of this variable, there is surprisingly little research on how to promote it effectively in many developing countries. The problems facing the youth in the labour market has become more intense as a result, youths turn to less productive and less remunerative work at the informal sector. This paper therefore investigates the link between Governance, Youth Employment, Gross Capital Formation and Economic Growth. It utilizes the Granger non-Causality technique to explore the connection between these factors in sets. The discoveries uncover that there is bi-directional causal connection among governance and economic growth and furthermore between Economic growth and youth employment in Nigeria. The causality between Economic growth and capital formation is uni-directional from gross capital formation to Economic growth. It is discovered that there is no causal connection among employment and governance; and among employment and gross capital. It is recommended that the government should put on policies to increase growth so as to increase youth employment. Since capital formation causes growth and growth in turn causes youth employment; this implies that more investment in the country will indirectly cause youth employment. Government policies aimed at boosting both public and private investments in the country should be formulated; consequently the challenges of youth unemployment would be addressed.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".