The Education for Peace integrative curriculum: concepts, contents and efficacy
Bibliographic record
Abstract
This article presents the conceptual foundations of the Education for Peace (EFP) integrative curriculum, reviews its contents, and briefly describes its impact on students, teachers, staff and parents/guardians in Bosnia and Herzegovina. The curriculum was developed in 2000, first employed in six pilot schools and then implemented in 112 primary and secondary schools in Bosnia and Herzegovina as well as in a few schools in North America, thus far involving thousands of educators and tens of thousands of students. The curriculum is being published in nine volumes covering core aspects of peace education. This article reviews the curriculum’s comprehensive and inclusive pedagogical approaches and unique conceptual formulation, which defines conflict as the absence of unity and unity as the main prerequisite for peace. The curriculum integrates insights from a wide range of disciplines on peace and education, including education, peace studies, conflict resolution, political science, law, religion, sociology, psychology and history. Within this integrative approach, each volume addresses one or more aspects of peace and peace education, drawing from the latest developments in the field and from lessons learned in the implementation of the EFP program. The final part of the article includes several first‐hand statements attesting the efficacy of the EFP program.
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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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".