A Cultural Metamorphosis: From Indigenous School Dropout to Educational Leadership
Bibliographic record
Abstract
This dissertation is my story of being a school dropout in the early 1960s and the journey I would travel toward achieving a successful career in the provincial education system as an educational assistant, an educator, and a school-based administrator, then as a director of education in a federal First Nation education system from which I retired in December 2020. After forty years of educational service, I took on a personal challenge to achieve the epitome in education attainment, a doctorate in educational administration at the University of Saskatchewan. During my years of service, I developed an advocacy for equitable educational opportunities for marginalized students. In particular, I was concerned about the consistently lower graduation and higher dropout rates for First Nations students compared to non-First Nation students. As an Indigenous student, at age fifteen, I removed myself from the subjugation of a colonized educational system that I now perceive was fraught with systemic and epistemic injustices. As a Nêhiyaw Cree educator, I had the opportunity to serve hundreds of Indigenous students (and non-Indigenous) in provincial and federal schools, many who would also leave school before graduation, further causing the disparity gaps between the two groups of learners. As a Nêhiyaw administrator, my mission was to create learning environments that provided equitable education opportunities where Indigenous students (and of course, all students) were encouraged and supported to stay in school, graduate, and realize that their dreams were possible. This autoethnography discusses the struggles, challenges, and perceived limitations I would encounter and overcome as I journeyed through life with a grade eight education to writing this dissertation for a doctorate degree in educational leadership. Guiding my research are the questions: 1) what underlying principle(s) is/are at the root of the alarmingly low graduation rates and the alarmingly high dropout rates for Indigenous students, 2) how can telling my story invoke systemic educational change to improve graduation rates for Indigenous students, and 3) how can telling my story provide inspiration and hope to others with similar backgrounds, Indigenous or non-Indigenous. My response to these questions lies within my autoethnography, where I reveal my lived experiences in search of my inherent identity, place, purpose and path that subsequently led me to a leadership career in education. This is tâpwê-nitâcimowin (my true story).
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.032 | 0.016 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.002 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 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".