Undergraduate Students’ Infractions and the Administration of Social Justice in Nigerian Universities
Notice bibliographique
Résumé
<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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».