How can we incorporate Indigenous perspectives into science courses?
Bibliographic record
Abstract
In recent years, post-secondary institutions across Canada have emphasized the importance of incorporating Indigenous perspectives into curricula. Science instructors may be uncertain about how to successfully integrate Indigenous traditions and ways of knowing with concepts derived from the scientific method while remaining respectful of and true to both approaches. In this session, I would like to spark discussion by presenting my initial attempts to incorporate Indigenous perspectives into an introductory biochemistry course. I will describe how I approached the land acknowledgement, give examples of how I connected traditional Indigenous practices to scientific concepts presented in the course, and show how students were encouraged to independently explore connections between biochemistry and Indigenous traditions through an open-ended assignment. Session attendees will be encouraged to share their own approaches and ideas in this area and reflect on changes they could make to their own courses. Through this session, I hope that all in attendance will progress in their thinking about how to create science courses through which non-Indigenous students will gain appreciation for Indigenous ways of knowing, and Indigenous students will feel a greater sense of belonging.
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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 | 0.020 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.013 |
| Scholarly communication | 0.010 | 0.017 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".