Constructive Conflict Talk in Classrooms: Divergent Approaches to Addressing Divergent Perspectives
Bibliographic record
Abstract
Dialogue about social and political conflicts is a key element of democratic citizenship education that is frequently advocated in scholarship but rarely fully implemented, especially in classrooms populated by ethnically and economically heterogeneous students. Qualitative case studies describe the contrasting ways 2 primary and 2 middle-grade teachers in urban Canadian public schools infused conflict dialogue pedagogies into their implemented curricula. These lessons, introducing conflict communication skills and/or content knowledge embodying conflicting viewpoints as learning opportunities, actively engaged a wide range of students. At the same time, even these purposively selected teachers did not often facilitate sustained, inclusive, critical, and imaginative exchange or deliberation about heartfelt disagreements, nor did they probe the diversity and equity questions surrounding these issues. The case studies illustrate a democratic education dilemma: Even in the classrooms of skilled and committed teachers, opportunities for recognition of contrasting perspectives and discussion of social conflicts may not necessarily develop into sustained democratic dialogue nor interrupt prevailing patterns of disengagement and inequity.
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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 | 0.040 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.034 | 0.075 |
| Scholarly communication | 0.032 | 0.015 |
| Open science | 0.008 | 0.038 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 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".