Indigenous Feminist Evaluation Methods: A Case Study in “My Two Aunties”
Bibliographic record
Abstract
This paper offers some key characteristics of Indigenous feminist approaches to evaluation and spotlights a unique and promising example of Indigenous feminist evaluation methods in the My Two Aunties (M2A) program. Though Indigenous feminist evaluation methods are diverse, complex, and community-specific, some general characteristics we point to in this analysis are commitments to anti-colonial conceptions of family, gender, and belonging, an assertion of the epistemic and evaluative importance of felt knowledge, the explicit confrontation of settler colonialism’s impact on Indigenous life, and the commitment to the transformative potential of community-led caretaking. We then turn to what we see as an exemplar of Indigenous feminist evaluation methods—the evaluation component of the My Two Aunties (M2A) program. Our paper will provide theoretical scaffolding for Indigenous feminist evaluation and add to the growing body of Indigenous scholarship that challenges what “counts” as evidence in settler scholarship arenas.
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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 | 0.041 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".