Lessons from Our Sweetgrass Baskets: A Wholistic Vision of Academic Success for Indigenous Women in Higher Education
Bibliographic record
Abstract
This qualitative inquiry documents the lessons gleaned from my journey toward the praxis of Indigenous Maternal Pedagogies, an Indigenous women-centred teaching and learning engagement, to offer insights for supporting Indigenous women in higher education. Specifically, this article offers an express vision for Indigenous women’s educational access and success in higher education by sharing a collective research story offered by Indigenous women participants who completed one or more of three courses related to Indigenous women’s literatures and Indigenous maternal theory. Each course was delivered through a decolonial feminist lens, comprised of Indigenous curricular content and engaged students in culturally relevant assessment. This work connects Maternal Pedagogies with Indigenous epistemologies that embrace the “whole student” within educational contexts to establish a teaching and learning environment that can speak to the hearts and minds of students. In the spirit of reconciliation, I position this environment as a safe space where students can be their whole authentic selves and where their realities and lived experiences are positioned as strengths and key assets to establishing an ethical space for cross-cultural and anti-racist dialogue. Collectively, the participant narratives offer four key lessons that are integral to reconciliation education more broadly, and I map these lessons as final recommendations that align with Kirkness and Barnhardt’s timeless work on the “Four Rs” of respect, relevance, reciprocity, and responsibility.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.026 | 0.027 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".