Road to Peace Education: Peace and Violence from the Viewpoint of Children
Bibliographic record
Abstract
It is important to adopt the concept of peace as a culture when human rights, democracy, coexistence and diversity are respected at the social level. Particularly at an early age, introducing this concept to individuals can prevent violent cultures from finding social or individual support. In this sense, individuals are expected to disseminate peace through education and to exclude violence. In this research, it was tried to show how primary school students perceive the concepts of peace and violence in their daily lives. It has been tried to determine how pupils describe these concepts in their pictorial image, literary and verbal expressions. The research was designed as qualitative research from qualitative research approaches. 68 primary school students participated in the research. Students have identified four main themes on the peace issue: “universal / inter-communal peace, inter-group / social peace, inter-personal peace and individual peace.” Twenty-five sub-themes related to these 4 main themes have been created. As for violence, four main themes have emerged: “socio-cultural violence, direct violence, group violence and ecological violence”. Sixteen sub-themes have been expressed, depending on these four main themes. It has been found out that in the general sense, they perceive the concept of peace mostly in a personal-individual sense and they directly perceive the concept of violence as socio-cultural violence.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.006 |
| 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".