Asking the ‘who’: a restorative purpose for education based on relational pedagogy and conflict dialogue
Bibliographic record
Abstract
Asking the ‘who’: a restorative purpose for education based on relational pedagogy and conflict dialogue Drawing upon Gert Biesta’s concept of the learnification of education, we maintain that a meaningful purpose for Canadian schools has been lost. We demonstrate that the very fact of relationship is limited in curricula. The absence of relationality enables the continued privilege of normative identities. A restorative approach, based on asking who is being educated, could repurpose schooling. We draw upon examples from literature, current political events and our classroom-based research to illustrate how conflict dialogue, based on relational pedagogy, offers one path for a restorative approach. We conclude that conflict dialogue provides opportunities to engage diverse students in inclusive curricular experiences. Such a restorative approach exposes and explores the who of education for the purpose of promoting positive social conditions that allow for human flourishing.
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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 | 0.015 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.019 | 0.085 |
| Scholarly communication | 0.016 | 0.016 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.006 | 0.011 |
| 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".