Exploring Reconciliatory Pedagogy and Its Possibilities through Educator-led Praxis
Bibliographic record
Abstract
In the spirit of taking an action-based response to the Truth and Reconciliation Commission’s (2015) Calls to Action and principles, a group of educators came together in 2016 to create a one-year graduate pathway program that sets students on paths toward reconciliation. By examining the contributions of international and national scholars who explore topics of reconciliation, we extend this global discussion with insights gained from our praxis-based approach in education. Inspired by Chung (2016), we present a model which identifies a set of entry points into the work of reconciliation: listening and learning from Indigenous peoples; walking with and learning from Indigenous peoples; and, working with and learning from Indigenous peoples. By examining what we have learned through our program “Indigenous education: A call to action,” our model posits that reconciliation is accessible to those who are willing to listen and learn, and, most importantly, take action.
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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 | 0.029 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.043 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".