Contributions of the Productive Sectors’ to the Nigeria Economic Performance
Bibliographic record
Abstract
The study empirically examined the contributions of the productive sectors’ to the Nigeria economic performance from 1981 to 2016. The study gathered time-series data majorly from the Central Bank of Nigeria Statistical Bulletin. The model in the study specified total gross domestic product of Nigeria as a function of the contributions of the manufacturing, agricultural, oil and gas, building, transport and trading sectors in the Nigerian economy. Employing the classical Ordinary Least Square estimates, ADF unit root test, Johansen Co-integration estimation techniques and Error Correction Modelling to analyse the data obtained. Based on the parsimonious error correction result, the study empirically explored that the ECM is correctly signed and significant and all the explanatory variables were positively and significantly related to the total GDP a proxy of economic performance in Nigeria. The study concluded that the productive sectors in Nigeria exert positive and significant influence on the Nigerian economy for the period under investigation. The study recommended, inter alia, that the government and all other stakeholders should channel huge economic resources into investing more in the productive sectors, so that these sectors will bring about the desired level of economic growth in Nigeria, as witnessed in the European world.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".