Examining the Braiding and Weaving of Indigenous Ways of Knowing, Being, and Doing in Alberta Teacher Education
Bibliographic record
Abstract
Alberta’s Teaching Quality Standard requires that all teachers possess and apply a foundational knowledge of Indigenous Peoples to their teaching. In 2020, representatives from ten Alberta teacher education programs came together to examine how they were braiding and weaving Indigenous ways of knowing, being, and doing into their programs. They also considered the challenges and successes encountered and the ways programs might work together to improve and combat anti-Indigenous racism. Drawing upon a collective case study methodology, representatives responsible for the design and delivery of Indigenous education within each of the programs completed an 18-question survey. Results demonstrate the Teaching Quality Standard (Alberta Education, 2018) served as a catalyst for deepening Indigenous ways of knowing, being and doing in preservice teacher training. The levels of integration are examined through the concept of differentiation (Tomlinson & Imbeau, 2010) and Kanu’s (2011) five levels of integration. Keywords: Indigenous ways of knowing, being and doing, teacher education programs, Teaching Quality Standard La norme de qualité pour l'enseignement de l'Alberta exige que tous les enseignants possèdent et appliquent une connaissance fondamentale des peuples autochtones dans leur enseignement. En 2020, des représentants de dix programmes de formation des enseignants de l'Alberta se sont réunis pour examiner la façon dont ils intègrent à leurs programmes les façons autochtones de savoir, d'être et de faire. Ils se sont également penchés sur les défis et les réussites rencontrés et sur les façons dont les programmes pourraient collaborer pour améliorer et combattre le racisme anti-autochtone. En s'appuyant sur une méthodologie d'étude de cas collective, les représentants responsables de la conception et de la prestation de l'éducation portant sur les autochtones dans chacun des programmes ont répondu à un sondage de 18 questions. Les résultats démontrent que la norme de qualité de l'enseignement (Alberta Education, 2018) a servi de catalyseur pour approfondir les façons autochtones de savoir, d'être et de faire dans la formation initiale des enseignants. Les niveaux d'intégration sont examinés à travers le concept de différenciation (Tomlinson et Imbeau, 2010) et les cinq niveaux d'intégration de Kanu (2011). Mots clés : Façons autochtones de savoir, d'être et de faire; programmes de formation des enseignants; norme de qualité pour l'enseignement
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".