Does Tertiary Enrolment Cause Growth in Nigeria? A Vector Auto Regression Mechanism
Bibliographic record
Abstract
The trend in tertiary enrolment indicates that even though it has nominally been increasing, in real terms, it is abysmally nose-diving. This is largely due, in part, to the declining trend of the budgetary allocation to education in Nigeria. Against this backdrop, this paper investigates the causal effects of tertiary enrolment on economic growth in Nigeria between 1980 and 2010. The study utilized the VAR approach and the VAR-Granger causality test to analyze the empirical model of the study. The findings of the empirical investigation suggest that there is a uni-directional causality between tertiary enrolment and economic growth in Nigeria. The causality runs from economic growth to tertiary enrolment in Nigeria. The paper therefore recommends that there is need for government to genuinely be committed to funding the educational sector if the aspiration to be among the twenty most developed economies by the year 2020 will not be a mirage.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".