Investment in Education for The Nigerian Economic Development
Bibliographic record
Abstract
This study examines investment in education for economic development of Nigeria. Education has been recognized globally as a veritable and strategic venture pivotal to economic transformation of any nation. The study made use of secondary data sourced from Ministry of Education, National Universities Commission (NUC) and Tertiary Education Trust Fund (TETFUND) and an Ordinary Least Square (OLS) regression method was used to analyze the data obtained to show the relationship between enrolments and funding. The result shows that the education sector contribute significantly to economic development as measured by the Gross Domestic Product although the sector is still underfunded most especially the basic and senior secondary levels in view of geometric increase in yearly enrollments and poor infrastructural facilities. The study recommends that the government at all levels should invest more in education and also collaborate with private sector through Private Public Partnership (PPP) initiative to accumulate the much needed funding that will pave way for technological development. It will also guide against brain-drains and significantly alleviate overdependence on aids from the developed nations and educational organizations.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".