Walking Alongside Three Inuit Women’s Educational and Employment Experiences as a Non-Indigenous Researcher
Bibliographic record
Abstract
My research focuses on the experiences of three Inuit women who participated in an early childhood diploma program that I was honoured to co-instruct with an Inuit Elder in 2016. These program participants had a strong passion and desire to further their education beyond secondary school. The research question for this study is: How do Inuit women’s post-secondary educational experiences demonstrate a reconciliatory approach to education that fosters self-reliance and community engagement? I use Indigenous Storywork, an Indigenous research methodology, in that the “intent is for storytellers, learners, researchers, and educators to engage with Indigenous stories for meaningful education and research” (Archibald et al., 2019, p. 1). In Chapter One, I present the significance of my research purpose and the experiences that I bring to my research. I then investigate Nunavut Territory’ historical context, Arctic Residential Schools history, and Nunavut’s adult educational policy statements (Government of Nunavut, n.d.) that apply to post- secondary education research in Chapter Two. In Chapter Three, I inspect the history of post- secondary education in Canada’s three territories. I examine Inuit women’s roles in traditional land work, wage work, and domestic work. As well, I investigate certain barriers that hinder Inuit women’s post-secondary education attendance and employment opportunities. I argue that post- secondary education programming is notable because its vocational merit benefits the entire Pond Inlet community. To support this argument, I examine various literature regarding Indigenous-focused culturally appropriate education and training. I also review the literature on Indigenous women’s sociocultural approach to education and work. I provide details of my theoretical framework, research methodology, and research design in Chapter Four. In Chapter\nFive, I focus on government-mandated education and employment policies and the implementation of these initiatives through the three participants’ education and employment stories. My participants’ stories bring significant insight into the Inuit Qaujimajatuqangit [Inuit traditional knowledge] principles and how these values are lived out in personal and professional lives in community and in their workplace. In Chapter Six, I present my concluding comments and recommendations for future research.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.047 | 0.016 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".