The Implication of Corruption on Economic Progress of Nigeria
Bibliographic record
Abstract
The issue of corruption is a universal challenge and has denied many emerging economies good business opportunities. This study makes use of the position of Nigeria in the country corruption ranking captured by Transparency International and the rate of corruption prevailing in the country to assess the degree of influence corruption has on economic growth of the country. The study employs secondary form of data obtained from World Bank Development Indicators and Transparency International which cover a period from 2008 to 2018. The regression result indicates that the country corruption ranking has a significant negative influence on economic growth in Nigeria while the rate of corruption prevailing in the country has a significant positive impact on economic growth in the country. The two results are significant and so the study concludes that the image of the country has been tarnished globally due to the high level of corruption in Nigeria and as internationally perceived. As a result, important investment opportunities elude the country even though the economy is growing with the high rate of corruption prevailing in the country. The study thus, recommends among others that the religious leaders and non-governmental organizations should assist in curbing the menace of corruption by inculcating moral values in the young generation who should grow up to say no to corruption and its attractions. This will go a long way to salvage the future of this great nation.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".