What Can Indigenous Feminist Knowledge and Practices Bring to “indigenizing” the Academy?
Bibliographic record
Abstract
More than a decade has passed since North American Indigenous scholars began a public dialogue on how we might “Indigenize the academy.” Discussions around how to “Indigenize” and whether it’s possible to “decolonize” the academy in Canada have proliferated as a result of the Truth and Reconciliation of Canada (TRC), which calls upon Canadians to learn the truth about colonial relations and reconcile the damage that is ongoing. Indigenous scholars are increasingly leading and writing about efforts in their institutions; efforts include land- and Indigenous language-based pedagogies, transformative community-based research, Indigenous theorizing, and dual governance structures. Kim Anderson’s paper invites dialogue about how Indigenous feminist approaches can spark unique Indigenizing practices, with a focus on how we might activate Indigenous feminist spaces and places in the academy. In their responses, Elena Flores Ruíz uses Mexican feminist Indigenizing discourse to ask what can be done to promote plurifeminist indigenizing practices and North-South dialogues that acknowledge dynamic Indigenous pasts and diverse contexts for present interactions on Turtle Island. Georgina Tuari Stewart proceeds to describe Mana Wahine indigenous feminist theory from Aotearoa before proceeding to develop a “kitchen logic” of mana, which parallels Anderson’s understanding of tawow. Finally, Madina Tlostanova reflects on how several ways of advancing indigenous feminist academic activism described by Anderson intersect with examples from her own native Adyghe indigenous culture divided between the neocolonial situation and the post-Soviet trauma.
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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 | 0.011 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.028 | 0.075 |
| Scholarly communication | 0.019 | 0.018 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".