Inner Spirit/Fire and Indigenous Student/Researcher Identity: Differing Spaces from an Ojibway Perspective
Bibliographic record
Abstract
This research explored my personal journey as an Indigenous student traversing a doctoral program in a Canadian university. Contrary to ample literature on Indigenous students portrayed from a deficit standpoint, my research offers an alternative narrative by expounding on areas that kept my Inner/Spirit Fire burning and contributed to my success. In the spirit of reconciling educative spaces, I employed Etuaptmumk/Two-Eyed Seeing (E/TES) as a theoretical framework to bridge and equate Indigenous knowledge with western knowledge in the academy. Honouring oral traditions of the Anishinaabeg, I use Indigenous autoethnography (IA) coupled with arts-based research to tell my story. Gathering data, I engaged in ceremony and used creative images/artwork to convey my truths through lived experiences and realities. I thus employed reflexive thematic analysis (RTA) as it specifically draws from a researcher’s cultural background and personal experience to interpret the data, enabling me to present authentic Ojibway-Anishinaabe perceptions. This research offers insightful threads that cross time and space, acknowledge the power of relationships and story, and recognize other-than human “beings” and realms as part of our Earth Walk. Further, themes indicate educative institutions can become sites of reclamation for Indigenous persons and students in a Eurocentric academic environment. Thus, I situate myself within an Indigenous concept of Seven Forward Seven Back to honour my ancestors and welcome emerging and future Indigenous scholars to support cultural survivance.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.034 | 0.044 |
| Scholarly communication | 0.015 | 0.007 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".