Non-Indigenous Women Teaching Indigenous Education: A Duoethnographic Exploration of Untold Stories
Bibliographic record
Abstract
Identifying as non-Indigenous, we are often left considering our positionality and identity inIndigenous education, how we have come to be invested in this area of research, and what we seeas our contribution. In conversation with one another, we realized we choose to share certainstories and not others about our experiences working in Indigenous education, but were lessfamiliar with why, after working in the field for a sustainable period of time, we felt the need tocensor our stories. What did we fear might happen if we divulged these ‘untold’ stories? Whatfollows is a duoethnographic inquiry that seeks to attend to this question. We have chosen todialogically document, analyze, and probe our experiences as teacher-educators in Indigenouseducation to unpack why we refrain from sharing certain experiences we have encountered sincebecoming involved in teacher education. By responding to this question through duoethnographicwriting we hope to broaden how we come to understand and extract meaning from our experiencesworking in the area of Indigenous education.
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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 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.029 | 0.025 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.003 | 0.006 |
| 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".