Undergraduate Students’ Infractions and the Administration of Social Justice in Nigerian Universities
Bibliographic record
Abstract
<p>Nigeria, situated in the West African sub-region of the African continent has an estimated population of over 170 million people with 146 universities. The demand for these universities in the recent past has been unprecedented with an average of 1.5 million applicants for placement annually, the highest anywhere in the world. Regrettably, public funding of the university is grossly inadequate with public expenditure per students as low as N168,000.00 or $1000 (given an exchange rate of N282 to $1). The consequences of the seeming inadequate funding manifest in overcrowded classrooms, inadequate facilities and unfriendly school climate with the attendant restiveness characterizing the behavior of students in the universities. It is the concern expressed by stakeholders about fairness in the administration of social justice that provides the motivation for studies. Three research questions were raised to guide the study perceived to be significant to university administrators, students’ union governments, security agencies and the society at large. The survey research design was chosen to observe and describe the situation of students’ disciplinary problems and the ability of university authorities to fairly administer justice to sanctioning infractions. A questionnaire titled: Students’ Infractions and Administration of Social Justice Questionnaire (SISAQUE) was designed and administered on 454 students and 146 members of Students’ Disciplinary Committees (SDCs) in the sampled schools bringing the total sample size for the study to 600. Data collected were analyzed with percentages, means and standard deviation. The result of data analysis showed that stealing, fighting, involvement in examination mal-practices, battery, intimidation, bullying, cyber crimes and involvement in cultism were the commonly reported infractions in the universities. It was also found among others that students have concerns about fairness in the administration of social justice in the universities. Based on the findings, it was recommended that other tertiary institutions such as polytechnics and colleges be fully developed to attract students with the belief that the unprecedented demand for university education in Nigeria will reduce drastically. It was also recommended that the culture of the rule of law be institutionalized in the process of administering social justice in the universities.</p>
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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".