Braiding Indigenous knowledge systems and Western science through co-creation and co-teaching
Bibliographic record
Abstract
Co-production of Indigenous Knowledge Systems with Western science is increasingly recognised as an important component of education and research. When done correctly, it draws on the strengths of the respective knowledge systems, ensures Indigenous data sovereignty, empowers communities, supports reconciliation, and fosters mutual respect. However, despite these clear benefits and alignment with the United Nations Declaration of the Rights of Indigenous Peoples, few examples, guidance, or frameworks exist, especially in the context of science education. Here, we illustrate how co-designing and co-teaching courses can effectively enhance knowledge systems. We show that students value the weaving of Indigenous Knowledge with science, both within (Westernised) academic settings and during place-based experiential learning. It can deepen connections to Indigenous ways of knowing and provides a source of healing as co-production studies are re-connections to Indigenous history and identity. We conclude by addressing some of the challenges faced and provide some actionable solutions for the global effort needed to decolonise and Indigenise both research and 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 | 0.017 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.047 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.002 | 0.023 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".