Dialogue-Centred, Collaborative Teacher Professional Learning: To Educate for Democratic Peace in Egypt and Canada
Bibliographic record
Abstract
Democratic Peacebuilding citizenship (DPBC) education incorporates essential components of: community building, conflict, diversity and communication, to promote democratic behavior and values, such as agency and decision making (tools to mobilize social change). Teaching for DPBC involves general and specific teaching techniques and pedagogical practices teachers use to attain this former goal. This case study with a critical perspective examined how two groups of 3-4teachers (from multiple private schools in Cairo and the greater Toronto area) voluntarily embarked on a journey of dialogic non-formal professional learning to educate for democratic peace. I facilitated a six-week online course with each group of teachers, introducing, enacting (co-adapting to each context) collaborative, dialogic professional development for DPBC education. Data collected included asynchronous discussion transcripts and semi-structured interviews prior and post participation in the module. Findings from this study indicated that teachers used similar techniques to teach for DPBC in the two contexts, with differences in the types of conflicts addressed and their cultural relevance to each context. Teachers
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.017 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".