Bibliographic record
Abstract
Human Capital is an integral part of any country’s development and economic growth has human capital as an important factor. This study evaluates the relevance of human capital development on the growth of the economy pin pointing the relationship that exists between them. In this study, the ordinary least square (OLS) technique was adopted. The GDP was used as a proxy for economic growth; Per Capital Real Gross Domestic Product, primary school enrolment, public expenditure on education and health, life expectancy, stock of physical capital as proxy for human capital. From the analysis, it was deduced that there is a strong positive relationship between human capital development and economic growth. The recommendations drawn from the study centered on revisiting the man-power needs of the various sectors of the economy. Also, while workable policies should be put in place to bring about an overall economic growth, expenditures on health and public education should be utilized effectively and efficiently so that the country would experience quality health care services and quality educational system. Key words : Human Capital, GDP, Economic growth, Ordinary Least Squares, Nigeria
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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".