Teaching Aboriginal education : responding to the Truth and Reconciliation Commission's calls to action for early childhood classrooms
Bibliographic record
Abstract
In response to the Truth and Reconciliation Commission (2015) Calls to Action, my capstone project explores ways in which to implement Aboriginal pedagogy in early childhood programs (Pre K-K) in the province of Saskatchewan, Canada. Grounded within socio-cultural theory, I focus on four aspects of Aboriginal pedagogy: relationships with family and community, experiential learning, storytelling, and relationship with the land. From the literature reviewed and the resources gathered, I found that implementing Aboriginal pedagogy, connected with place-based education and Social and Emotional Learning (SEL) into early childhood classrooms enhances children’s understanding of the cultural context, and strengthens their relationship with, the place in which they live. In connecting this research to practice, I included my own experiences as a parent and a teacher, as well as draw from examples of early childhood educators who are currently incorporating Aboriginal pedagogy, history, and culture into their programs. As an outcome of this project, I developed a website that is focused on implementing Aboriginal pedagogy in early childhood (Pre K - K) in Saskatchewan in order to support educators, and as a response to the TRC’s (2015) Call to Action in education to share lesson plans and best practices. Based on the findings from the literature reviewed, I recommend pre-service and in-service education on Aboriginal pedagogy, culture, and history, with a focus on place-based education, and building children’s capacity for empathy to support both Aboriginal and non-Aboriginal young learners.
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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 | 0.043 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.037 | 0.021 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.005 | 0.018 |
| Research integrity | 0.010 | 0.020 |
| Insufficient payload (model declined to judge) | 0.004 | 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".