Derivation Funds Management and Economic Development of Nigeria: Evidence From Niger Delta States of Nigeria
Bibliographic record
Abstract
Economic Development of any nation depends on the efficient use of available resources and the integrity of people entrusted with the management of those resources. This paper investigated the impact of the Management of derivation funds accruable to Niger Delta States and how it affects Economic Development of Nigeria. The study employed a descriptive research design and made use of Ordinary Least Squares (OLS) technique to test the hypothesis. The time series data used covered a period from 1981 to 2016 and were collected from the Central Bank of Nigeria (CBN) Statistical Bulletins and World Bank reports. The data gathered were on Real Gross Domestic Product (RGDP) which is the dependent variable and Niger Delta States Derivation Funds (NDSDF) as the explanatory variable. The regression result revealed a positive relationship between the RGDP and NDSDF. The study also found evidence that NDSDF has significant positive impact on the RGDP. These findings led to a conclusion that the lack of infrastructures and other physical evidences of Economic Development in the Niger Delta States have been as a result of mismanagement of funds and embezzlement. If the derivation allowance is well utilized the economic well-being of the people in the area will improve and the clamour for resource control will cease.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".