Bibliographic record
Abstract
This study examines the impact of Human Capital Development on Economic Growth in Nigeria.It made use of five variable ordinary least square regression models to test the impact of Human Capital Development on the Growth of the Nigerian economy.These variables are Government Expenditure on Health (GEHT), Government Expenditure on Education (GEED), Labour Force (LF), Life Expectancy (LE), and Gross Rate of Capital (GRC).Secondary data sourced from the Central Bank of Nigeria Statistical Bulletin, 2013, National Bureau of Statistics report 2013 and World Bank Annual Report, 2013 was used in the study.It was also found out that about 91% of the changes in the dependent variable (GDP) were accounted for by changes in the explanatory variable.This to a large extent explains the place of human capital development in economic growth.The more the government concentrates on human capital development, the more of economic progress that will be recorded.The study therefore recommends as follows: that priority should be given to human capital development considering the impact it had on economic development as exposed by the result of the finding.Both formal and informal education should be developed to increase its impact of the nation's economy and also Government should redouble their effort toward improving the standard of education in Nigeria.This will on the long run translate to economic growth occasioned by a vibrant and well trained manpower.It has been discovered that human resource rather than natural resources guarantee economic growth the world over.In a country like Nigeria where economic growth and development is much need, a study to expose the impact of human development on economic growth cannot but be significant.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.922 | 0.920 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".