School Administrators Strategies for Combating Corruption in Universities in Nigeria
Bibliographic record
Abstract
<p>The purpose of this study was to examine corruption in universities with the aim of finding out the types/forms, causes, effects and measures for combating the menace. Four research questions guided the investigation. The study is a survey research, ex-post facto in nature. A sample of 780 comprising of students, academic staff and administrative staff was selected through random sampling technique from six public universities in Nigeria. Data collected through the questionnaire was analyzed using descriptive statistics. Findings revealed that the types of corruption prevalent in universities are examination related, admission related, finance related, accreditation related and sexual related. Each of these types of corruption has different forms of manifestations. The causes of corruption in universities included greed, lack of fear of God, and the desire to get rich quick. The effects of corruption and measures for combating it were identified. The study concluded by recommending among others that all stake holders in university education should have a moral reorientation and begin to reverence God by fearing him to help sanitize the universities and create a corruption free learning environment in the university system.</p>
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".