Cultivating Circles of Indigenous Dialogue in Teacher Education
Bibliographic record
Abstract
This symposium explores productive scholarly relations with local Indigenous communities, teacher-learners, and a Canadian University within a Graduate Diploma. Through our collective attending to Reconciliation through Education, what is emerging is a powerful resurgence of Indigenous ways of being and pedagogies through the sharing of Indigenous Knowledges, cultural practices, ceremony, and language revitalization. The focus on Indigenous Worldviews, Knowledges & Perspectives, Circle Processes and Education for Reconciliation provides opportunities for teacher-learners to embed Indigenous Pedagogies and two-eyed seeing into their practice. Throughout the program teacher-learners: are invited into creative and critical conversations, explore circle pedagogies, participatory processes, place-based explorations, and intercultural dialogues with Indigenous Knowledge Holders and Elders; acknowledge and experience Indigenous protocols and ceremony; and actively explore Indigenous teachings. The focus is on creating authentic Indigenous-learning pathways and to do so in relation to a particular place, peoples, and its unique cultural ecology. We will share an Indigenous Metissage, which will weave together narrative threads of the emerging story of the collaboration and the actual voices and experiences of the teacher-learners within the program. Members of this family will stand to share and give witness to the profound learnings that have emerged from the journey.
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.032 | 0.035 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.002 | 0.022 |
| Research integrity | 0.003 | 0.003 |
| 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".