The Effect of Corruption on Socio-Economic Development of Nigeria
Bibliographic record
Abstract
This study springs up because socio- economic development is very pertinent to all, hence it would be regarded as the apex of our social existence. It will help to identify how deeply corruption has really affected socio- economic development of Nigeria. However, this study aims to enable us know the causes of corruption, and to find out how deep corruption has eaten into the fabric of Nigeria’s society, etc. It also tends to examine the effect of corruption on Nigeria’s various socio- economic developments and tries to explore on the damage which this disease has caused to the image of the nation since 1985 up till the year 2010. By then, the effort of the anti- graft agencies EFCC and ICPC would also be examined to see if they have been efficient in discharging their duties, and to suggest the better way in more holistic and independent manner which would help as an encouragement for them to face the fight or war against corruption without fear or favour of the power that be. The result of this evaluation here is to show that this issue of corruption in Nigeria is an important national issue and has to be treated with all carefulness. There is great need for people to be shakier over the damage which corruption would cause if neglected. Based on the analysis of this work, government would know how best to fight corruption and alleviate the heat of it so as to build stronger and healthier economy. A lot of work would have been done on this subject; some might have done it with less seriousness. To allow for socio- economic development, this subject has to be treated objectively with utmost care. And it is that objectives I would say, this work have appeared to achieve it.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| 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".