Epistemic Injustice and Indigenous Women: Toward Centering Indigeneity in Social Work
Bibliographic record
Abstract
Using the theoretical framework of epistemic injustice articulated by philosopher Miranda Fricker as an analytic tool, we analyze recent victories of Indigenous feminist activism in gathering the stories of Indigenous women, challenging dominant meta-narratives and rewriting the herstory of Canada. We use the epistemic concept of the hermeneutic gap to consider the implications of this resistance in conjunction with the increased visibility of the intersectional positionality of Indigenous women. To illustrate our analysis, we focus on two case studies. Firstly, an individual perspective through the life journey of a feminist Anishinaabe Activist, Bridgett Perrier. Secondly, we conduct a systemic analysis of the recent Report on the National Inquiry into the Missing and Murdered Indigenous Women and Girls (MMIWG). We close with a discussion on how critical it is for social workers—especially non-Indigenous social workers—to relearn and document the meaning of the MMIWG issues. This includes recognizing Indigenous resistance, activism, and the newly formulated hermeneutic understandings that are emerging. Then, the final task is to apply these concepts to their practice and heed the calls to action which the report calls for.
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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 | 0.017 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.020 | 0.120 |
| Scholarly communication | 0.017 | 0.013 |
| Open science | 0.002 | 0.021 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".