The Effectiveness of Anti-corruption Agencies in Enhancing Good Governance and Sustainable Developmental Growth in Africa: The Nigeria Paradox under Obasanjo Administration, 2003-2007
Bibliographic record
Abstract
Nigeria’s public image at international scene has been very negatively, impressed. Since 1966 when the first military coup took place on the account of corruption, the country is still searching for better atmosphere that could guarantee corrupt free society. Since the early 2000’s when Transparency International ranked the country the second most corrupt nation in the world, the government has been working assiduously to make sure that the Nigeria image is being treated with respect. Nigeria and indeed Africa are so enmeshed in corruption that their leadership is either positioning for life leadership or wealth for great, great, grand children unborn. Also in these countries, hunger is so pronounced that people throw away conscience and decorum for survival. In a bid to fight these ills and join the wagon for good governance, viable democracy and developmental growth, some African countries like Ghana, South Africa, Nigeria, etc. have been making efforts towards attaining such desired governance. In Nigeria many anti corruption agencies are established, both public and private, all aimed at fighting against corruption and enthronement of better governance. In this paper some of these anti-corruption agencies are critiqued and their impacts are also empirically examined. Key Words: Anti-corruption agencies; Good governance; Sustainable development
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".