Constructive Conflict Pedagogies for Building Democratic Peace
Bibliographic record
Abstract
<JATS1:p>This open access book shows what teaching for democratic citizenship and peace can look like in classrooms in violent and less-violent contexts around the world.</JATS1:p> <JATS1:p>It features chapters written by leading scholars and practitioners based in Canada, Chile, Columbia, Cyprus, Mexico, Spain, the UK and the USA. It includes sections on navigating contested history and heritage; language teaching that bridges social identities; teaching democratic engagement with conflictual issues; and students Sharing Authority and Handling Systemic Violence. The chapters cover a wide range of topics with local and global significance including indigenous praxis as peace building, social conflicts, transformative hope, teacher training, and student voice. Vignettes of practice accompany each chapter, grounded in careful scholarship and teaching experience. The book shows how teachers and young people can feasibly nurture and learn non-violent ways of dealing with difficult conflicts and social tensions, to become agents of democratic revitalization and peacebuilding in their own communities and beyond. The contributors also engage critically with the UN Sustainable Development Goals (SDG), SDG 4 and 16 in particular, arguing that teachers and teacher educators can make a crucial difference in meeting these goals.</JATS1:p> <JATS1:p>The ebook editions of this book are available open access under a CC BY-NC-ND 4.0 licence on bloomsburycollections.com. Open access was funded by The Weatherhead Canada Program.</JATS1:p>
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.015 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.023 | 0.004 |
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".