Beyond the “Indigenizing the Academy” Trend: Learning from Indigenous Higher Education Land-Based and Intercultural Pedagogies to Build Trans-Systemic Decolonial Education.
Bibliographic record
Abstract
Given the UNDRIP’s assertion of Indigenous Peoples’ rights to their education and knowledge systems, and in the wake of the Truth and Reconciliation Commission of Canada’s calls to action, many Canadian Universities are considering “Indigenizing the Academy.” Yet, the meaning of such undertaking remains to be clarified. This article explores trans-systemic approaches as a possible avenue for “Indigenizing the Academy,” and, more specifically, what Indigenous higher education programs and institutions can contribute to a trans-systemic approach to education. Considering two existing models I encountered in my doctoral research, namely the Intercultural approach as developed in the Andes (García et al., 2004; Mato, 2009; Sarango, 2009; Walsh, 2012), and land-based pedagogy as developed in North America (Coulthard, 2017; Coulthard & Simpson, 2016; Tuck et al., 2014; Wildcat et al., 2014), I argue they present trans-systemic elements that would allow us to re-think the frameworks in which to engage with Indigenous Peoples’ rights and knowledge systems in the mainstream academy. What could be learned from the principles and practices of these two Indigenous higher education philosophies to articulate Indigenous knowledge into trans-systemic education in the mainstream academy in ways that foster solidarity and mutual understanding between Indigenous and non-Indigenous people?
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.048 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.003 | 0.008 |
| 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".