Mindfulness and Indigenous Knowledge: Shared Narratives About Reconciliation and Decolonization in Teacher Education
Bibliographic record
Abstract
This article discusses how shared narratives about mindfulness practices and Indigenous knowledge advance the reconciliation and the decolonization of Teacher Education curricula. We, the authors, experienced the beneficial impact of our personal mindfulness practices in nurturing and cultivating the harmonious balance of the physical, emotional, mental, and spiritual dimensions of the self. Within the context of the Truth and Reconciliation’s Calls to Action (2015), we observed the connections between mindfulness practices and local and place-based teachings of First Nation and Métis First Peoples in Northern British Columbia and in the Interior of British Columbia, Canada. Our experiences are informed by our personal mindfulness practices and from traditional and ancestral practices led by Elders and Knowledge Keepers. Our distinct narratives describe our learnings and our unlearnings as we participated in ceremony and listened and learnt from Elders and Knowledge Keepers of Syilx Okanagan Nation, Lheidli T’enneh First Nation, the Māori Nation, and the Métis Nation of Manitoba. By recognizing and respecting ancestral ways of doing and ways of being, we propose that contemplative practices like mindfulness can support a deeper understanding of how reconciliation and decolonizing are brought to the forefront of shared narratives in Teacher Education programs in the Okanagan and in Prince George.
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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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.023 | 0.049 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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".