Managing Tertiary Education for Peace and Conflict Resolution in Nigeria
Bibliographic record
Abstract
Peace is a necessary condition for the sustainable development of any nation. It is described as the absence of physical and structural violence, and the presence of justice. Peace education involves human rights and conflict resolution education. This justifies the prominence of peace and conflict resolution education in the educational agenda of nations. Based on this, the paper examines the management of tertiary education for peace and conflict resolution in Nigeria. The population of the study comprised lecturers from the Faculty of Social and Management Sciences from the Universities of Benin, Port Harcourt, Calabar and Uyo, totalling 2312. A sample of 231 lecturers was drawn for the study using the Cluster Sampling Technique. One research question and one null hypothesis were considered in this study. Data collection was done using a structured instrument tagged, "Managing Tertiary Education for Peace and Conflict Resolution" (MTEPCR) Questionnaire. The Instrument was duly validated and tested for reliability using the Cronbach Alpha reliability formula. This gave a reliability coefficient of 0.81. Descriptive statistics such as mean, standard deviation, and simple percentage were used to answer the research question. The null hypothesis was tested at 0.05 alpha level, the one-way ANOVA. The result of the study indicated a low extent in the implementation of peace and conflict resolution education in tertiary institutions. There was no substantial difference in the implementation of peace and conflict resolution education among four federal universities. Based on these findings, key policy, practice and research implications are discussed.
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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 | 0.002 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".