Being Métis: A music therapist’s experience of ancestry, spirituality and reconceptualization
Bibliographic record
Abstract
The Métis people are a distinct Indigenous group within Canada who identify with their own customs, traditions, and ways of knowing that include spiritual practices. This research aimed to explore spirituality in relation to the Métis culture, as I, a certified music therapist, explored my Métis ancestry including history, artistic culture, healing traditions, and ways of knowing. The data consisted of personal journal reflections, free writing and poetry. The analysis focused on how this impacted me personally, spiritually, and professionally. A heuristic self-inquiry research methodology was used and conducted in accordance with the guidelines established by Moustakas (1990). Data was analyzed using Neuman’s (2006) coding analysis methods. Findings elucidated three main categories of personal learning; learning from the outside-in, learning from the inside-out, and the extended journey. These findings have significant personal implications that will be discussed along with the clinical implications that the data analysis highlighted. Although there is a growing awareness of decolonizing methodologies and their place within educational institutions and government policies, literature on music therapy and the Métis culture is scant. Dr. Carolyn Kenny (1946-2017), a renowned music therapy pioneer and Indigenous scholar has provided a critical foundation that connects Indigenous ways of knowing to the music therapy research, theory, and practice. I aim to contribute to the literature through insights into my own spiritual well-being and by sharing the on-going intricacies and challenges of the Métis people of Canada, therefore implications for further research, education and training are discussed.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.022 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".