Challenges and Possibilities for Truth and Reconciliation In Teacher Education: An Engagement with the Literature
Bibliographic record
Abstract
This article delves into the evolving landscape of teacher education within the context of truth and reconciliation, acknowledging the profound role education has played in perpetuating colonial violence against Indigenous peoples. To assess reconciliation efforts in teacher education, a targeted search was undertaken, which resulted in an inductive thematic analysis of 36 scholarly works and the emergence of five overarching themes: anti-racist/anti-oppressive perspectives, decolonization, critical forms of pedagogy/narrativity, indigenization, and historical thinking. The analysis provides valuable insights and highlights challenges of advancing truth and reconciliation in education including the need for a paradigm shift within teacher education programs, urging them to adopt community-focused, land-based, and decolonizing approaches. By aligning with the spirit and intent of truth and reconciliation, and as the studies demonstrate, teacher education has the potential to contribute significantly to advancing the process of healing, justice, and mutual understanding in the journey toward a more equitable and harmonious future.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.033 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.025 | 0.090 |
| Scholarly communication | 0.032 | 0.031 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.002 | 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".