Bibliographic record
Abstract
The study ascertained the extent the accrued revenue generation has enhanced economic growth in Nigeria from 1981 to 2015.Secondary data sourced from Central Bank of Nigeria (CBN)'s Statistical Bulletin and National Bureau of Statistics (NBS) on such variables like real gross domestic product; oil revenue; non-oil revenue; labour force; social and economic services expenditure during the period were made use of.The collected data were analyzed using appropriate descriptive and inferential statistics like regression analysis and time series using E-views 9 statistical package.The Autoregressive Distributed Lag (ARDL) Model approach was adopted to consider the long run elasticity as well as the short dynamics among the variables of interest.The result of the analysis showed that labour force has a positive impact on economic growth in Nigeria during the time and there exist a significant positive relationship between oil revenue and economic growth in Nigeria.The gross fixed capital formation was found to be positively related to economic growth and the social and community services expenditure has a positive effect on the Nigerian economy.The non-oil revenue that was found to be negatively related to economic growth but not statistically significant implied possible leakages during the period.The study found that a positive relationship existed between oil revenue and economic growth in Nigeria while it was negative for the nonoil revenue between 1981 and 2015.Therefore, the study concluded that the accrued revenue had positive significant impact on economic growth of 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 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.930 | 0.917 |
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".