Decolonizing, Indigenizing, and Learning <i>Biskaaybiiyang</i> in the Field: Our Oral History Journey
Bibliographic record
Abstract
Centering Anishinaabeg and Ininiw ways of learning from, understanding, and sharing history, this article explores the oral history journeys of Lorraine Sutherland and Katrina Srigley on Nbisiing Anishinaabeg and Mushkegowuk territory in Ontario, Canada. The authors argue for the importance of a decolonized approach to feminist oral history, an approach which centers the histories of women and other marginalized voices in ways that acknowledge and mobilize Indigenous ways of understanding, documenting, and sharing stories of the past. The authors build this argument through stories, in the Anishinaabeg and Ininiw way; they offer a framework for drawing meaningfully on Anishinaabeg, Ininiw, and Western forms of feminist oral history.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".