Decolonizing Education Through Outdoor Learning: The Learning Story of an Indigenous Kindergarten Teacher
Bibliographic record
Abstract
This study examined the decolonizing pedagogy and practices of a First Nations \nkindergarten teacher. Indigenous communities across Canada and the world are currently \naffecting transformation in their schools by turning systems of colonial domination to \neducation that is locally controlled, culturally relevant, and empowering. The study \ninvestigated the teacher’s learning story, including her personal experiences with \neducation throughout her life, as well as her current practice as an educator, through both \nan Indigenous and non-Indigenous lens. The author and the teacher acted as coresearchers \nin this collaborative project. The exploration of this pedagogy and practice \nthrough these two perspectives sought to gain insight into potential solutions for \ndecolonizing education. This research is thus shared in the hope of bringing Indigenousdriven \nreconciliation into our classrooms by providing a decolonizing framework that can \nbe imparted to fellow educators. The researchers observed that decolonizing pedagogy, in \nthis instance, occurred through outdoor learning, culturally centred practices, as well as \nfamily and community connections. Such practices were determined to be deeply rooted \nin the teacher’s personal identity and experiences, stemming from an Indigenous \nepistemology and ontology.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.024 | 0.018 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".