Earth dance and fire song: A journey towards transformative reconciliation in nursing education
Bibliographic record
Abstract
G ̱ ilakas'la, Nugwa'a̱ m Joanna Elizabeth Fraser.I was born in East Africa to parents of European ancestry.I have been an inhabitant of the West Coast of Canada since I was two years old.This inquiry offers a vision for co-creating healing learning spaces for transformative reconciliation in nursing education.Oriented by Indigenous research methodologies, I draw from métissage and, portraiture to share the story of finding ya'xa̱ n yiyaḵ ̓ wima (my gifts from the Creator).Starting with finding ya'xa̱ n dłig ̱ a̱ m (my name), I ask who I am in relation to the places and people who I have learned from.In finding ya'xa̱ n ḵ ̓ a̱ ngex̱ tola (my blanket), I ask where I am from as I weave, unweave and reweave understandings of what I have learned as a nurse and as an educator.In finding ya'xa̱ n ya̱ xw'a̱ nye' (my dance), I ask where I am going and share my experiences from over 13 years of co-facilitating immersion learning field schools in remote First Nations communities.Finally, I share the learnings of my inquiry for educators more generally as I find ya'xa̱ n ḵ ̓ a̱ mda̱ m (my song) and ask myself why I am here.My learnings from the field schools are about following the lead of Indigenous people, orienting myself to relationships and always seeking out wellness.These learnings are applied to nursing education more generally as my inquiry leads me through three landscapes: bearing witness, being an inhabitant and becoming Indigenist.Transformative reconciliation happens when we naḵi'stamas (make things right) and tlaxwalapa (lift each other up with love).We can do this through living in relationally accountable and ecologically reciprocal ways.My lessons are of the Sisiutl, seeing everything in complexity, and of the he'istalis (world around us) experiencing everything as relationship.Ultimately, my vision is to reimagine nursing and nursing education so that we can heal ourselves, each other, and the land to become synala (whole).
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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 | 0.013 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.027 | 0.030 |
| Scholarly communication | 0.018 | 0.013 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.007 | 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".