External Financing of Budget on Sustainable Economic Growth in Nigeria
Bibliographic record
Abstract
The papers attempt to validate/invalidate economic growth sustainability vis-à-vis external financing of budget in Nigeria. The external financing channels - multilateral, Paris Club, London Club, promissory notes, bilateral, Euro bond, diaspora debts, and others - were tracked in relation to economic growth sustainability. The data is accessed from Emission Database for Global Atmospheric Research [EDGAR], the World Bank Development Indicator (WDI), and the Central Bank of Nigeria (CBN) statistical bulletin, for forty years (1981 to 2020). The study analysis follows plotting the visual trend of the series to ascertain its movement over time. Likewise, descriptive inference – skewness (sk), Kurtosis (k) & Jacque-Bera (JB) statistics were inferred for series normality. Also, Augmented Dickey-Fuller (ADF) unit root test, cointegration, vector autoregression (VAR), and the impulse response function (IRF) technique formed the basis of the estimation tools. Finding validates that there is no significant long-run relationship between external financing of the budget and sustainable economic growth in Nigeria. As a result, a reduction, and or a stop to further contracting external financing for budget purposes, and ensuring a funding-project-tied, is strongly recommended.
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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 | 0.003 |
| 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".