An Assessment of Corruption in the Public Sector in Nigeria: A Study of Akure South Local Government Area, Ondo State
Bibliographic record
Abstract
Issue of corruption in Nigeria has been a major concern to all and sundry in the society. The upsurge of this social menace in Nigeria in recent times is disturbing and it seems the menace has defied all kinds of treatment and the damage it has caused to national life cannot be quantified. The objective of this paper therefore, is to examine and assess corruption in the public sector in Nigeria with particular reference to Akure South Local Government Area of Ondo State. It has generated data based on questionnaires, on the public perception and level of corruption in the study area. The questionnaire and the groups tested were segmented. However, it was observed that many factors such as; lack of transparency, moral laxity, weak government institutions, unemployment and poverty etc were significant factors stimulating corruption in Nigeria. By this result, it means that proactive measure must be shaped towards eradicating corruption in Nigeria. This paper recommends, among others, that government should strengthen the institutions established to fight corruption.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".