Educational Reconciliation: Implementing Traditional Land-Based Learning in Canadian Universities
Bibliographic record
Abstract
Indigenous groups across Turtle Island are often marginalized in the creation of contemporary theories and policies, creating a knowledge gap of the traditional Indigenous ways of teaching and knowing in academia. Land-based learning encompasses the ways of knowing of Indigenous groups and when implemented as part of the ii' taa'poh'to'p, the University of Calgary Indigenous Strategic Plan promotes the decolonization of current pedagogies and the understanding of the importance of land to Indigenous peoples. We aimed to decolonize a university course through land-based learning and bridging the gap between Western and Indigenous knowledge while remaining respectful of Indigenous protocols and inflicted traumas by settler-colonial goals to extract knowledge from their communities. This study sought to research how land-based learning could be implemented into an Indigenous Studies course on animal-human relationships at the University of Calgary. Through a qualitative study of peer-reviewed sources predominantly written by Indigenous authors, we found common themes on how we could implement land-based learning into the course. Based on our findings, we proposed a three-part learning module. Firstly, the students should be introduced to the topic of land-based learning before attempting a land-based activity. Secondly, we suggest the incorporation of a land-based activity led by an Indigenous Knowledge Holder or Elder. Lastly, the learning module should be finalized with a class discussion and self-reflection assignment to provide students with the opportunity to solidify their learning. As Canada moves to reconcile a broken relationship with Indigenous groups and the Earth, the integration of traditional ways of knowing promotes the resurgence of Indigenous ways of being in education.
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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 | 0.016 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.028 | 0.008 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.006 | 0.012 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".