Peace Education Models in Enhancing Global Peace Awareness and Open Dialogue among Higher Education Students
Bibliographic record
Abstract
This bibliometric study examines the evolution of research on peace education frameworks, focusing on their role in enhancing global peace awareness and fostering open dialogue among higher education students. Analyzing publication trends from 1014 to 2024, the study identifies a significant increase in scholarly output, reflecting the growing academic interest in peace education. Thematic clustering reveals that violence prevention, critical pedagogy, ad teacher training remain central research themes, while emerging areas such as sustainability, global citizenship, and digital education indicate expanding interdisciplinary approaches. The study categorizes the most influential research areas, with Education Policy, Sociology, and Philosophy contributing the highest volume of publications and citations. Additionally, an analysis of influential journals highlights the dominance of the Journal of Peace Education, reinforcing its role in shaping discourse within the field. Findings further indicate that critical pace education models, holistic frameworks, and experiential learning strategies are the most widely used approaches in higher education. These models emphasize the integration of theoretical knowledge with practical applications to equip students with the skills necessary for conflict resolution and peacebuilding. Based on these insights, it is recommended that future research future explore interdisciplinary connections, expand regional representation, and integrate technological advancements to enhance the effectiveness of peace education. Strengthening institutional commitment to peace education initiatives can ensure that higher education continues to serve as a platform for fostering sustainable peace, intercultural dialogue, and social justice on a global scale.
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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 | 0.017 | 0.068 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.045 | 0.081 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".