The Impact of the Growth Rate of the Gross Domestic Product (GDP) on Poverty Reduction in Nigeria
Bibliographic record
Abstract
This study is on the impact of GDP growth rate on poverty reduction in Nigeria. The study made use of secondary data sourced from the Central Bank of Nigeria statistical bulletin and the National Bureau of Statistics between 1986 and 2012. The model for the study has as its dependent variable the Unemployment rate whose reduction should imply poverty reduction and its explanatory variable is the GDP growth rate. Using the Ordinary Least Square (OLS) regression techniques; our study revealed that the there is a weak relationship between the unemployment rate and the Nigerian Gross Domestic Product (GDP) growth rate and that instead of an inverse relationship, it was positive. That is, as GDP was growing the unemployment rate was also growing. The study holds that when citizen cannot work to earn they will remain poor. This implies that the GDP growth has not impacted positively on the poor through job creation sufficient enough to reduce the percentage of the unemployed and the incidence of poverty over the period of study. We therefore recommend that there is the need to reassess the growth direction so as to give priority to key sectors like the agricultural and the industrial sectors that have the capacity to generate and absorb more labours thereby solving employment problem and at the same reducing the poverty incidence on the citizens.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".