Mapping research on peace education: the bibliometric analysis for research agenda in the future
Bibliographic record
Abstract
This study aims to analyze the trend of scientific publications with the theme of peace education. This study uses bibliometric analysis to describe trends in peace education research and reveal its bibliometric profile. The data was taken from the Scopus database covering 1961 to 2023 with the keywords “peace education” and “violence.” The results of the analysis show that there is a positive trend toward an increase in publications with the theme of peace education. The most prominent country that contributes to peace educationthemed publications is the United States. The University of Toronto and the University of Kwazulu-Natal are the most famous universities that publish research results on peace education. The Journal of Peace Education is the favorite journal for publication on the theme of peace education. Vaughn Mitchell John and Johan Galtung are prominent names who have influenced publications on peace education. Potential themes regarding peace education are discussed in this paper. This research contributes to analyzing structure, trends, collaboration opportunities, and research roadmaps as a basis for future research.
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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 | 0.099 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.074 | 0.064 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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; both teacher heads agree on what is shown here.
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".