Reclaiming Relational Pedagogies: The Role of Talking Circles in Decolonizing Higher Education Classrooms
Bibliographic record
Abstract
This article explores the use of Talking Circles as a decolonizing pedagogical practice within higher education, grounded in Indigenous epistemologies and relational worldviews. Drawing on the works of Indigenous scholars such as Graveline, Wilson, Archibald, and Simpson, the article positions Talking Circles not merely as instructional strategies but as ceremonial, spirit-centered pedagogies that resist the Eurocentric hierarchical and individualistic norms of Western academia. Through autoethnographic reflection and student narratives from undergraduate and graduate education courses, the article demonstrates how Talking Circles foster relational accountability, ethical engagement, and transformative learning. By centering oral tradition, reciprocity, and communal learning, Talking Circles reconfigure the classroom as a space of collective inquiry and healing. The article also addresses the ethical considerations of integrating culturally grounded practices within institutional contexts, emphasizing the importance of protocol, consent, and community accountability. Ultimately, this work contributes to ongoing efforts in pedagogical resurgence and educational sovereignty, offering a framework for structurally embedding Indigenous ways of knowing in curriculum development, pedagogy, practice, and institutional change.
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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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.039 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.002 | 0.004 |
| 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".