Conversation of Images: Navigating Truth and Reconciliation Through the Arts and Education
Bibliographic record
Abstract
This article delves into some of the core tenets, aspirations, and objectives of the Truth and Reconciliation Commission (TRC), emphasizing its significance within the educational realm. Exploring the pedagogical inquiry implications for teachers and teacher educators, it exemplifies a teacher’s transformative journey towards embracing and embodying TRC principles. The narrative underscores the potential of arts in exploring TRC’s themes and highlights the pivotal role of education in fostering an understanding of reconciliation. This case study features a partnership between a high school teacher and an Indigenous artist. They collaborate to inspire youth and embrace community engagement alongside various local and Indigenous artists, knowledge keepers, activists, and other community members. The study showcases the impacts of arts in facilitating meaningful conversations on Truth and Reconciliation. This example underscores one teacher’s cultural and pedagogical inquiry and its importance on reimagining education as a platform for collaboration, community engagement, and social justice learning.
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 | 0.021 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.018 | 0.062 |
| Scholarly communication | 0.021 | 0.030 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".