Complementary Worldviews Aligning: A Relational Approach to STEM Education
Bibliographic record
Abstract
Abstract: Educators in Canada face federal and provincial mandates arising from the TRC Calls to Action (2012) to incorporate First Nations, Métis, and Inuit (FNMI) perspectives across the curriculum. Yet it is not uncommon to hear K–12 educators as well as some university faculty asking, "How do I incorporate Indigenous culture into science? Is it even possible?" This paper not only describes how it is possible but also explains why it is necessary, providing a theoretical framework and practical examples of how Western approaches to science, technology, engineering, and math (STEM) knowledge can be braided with Indigenous treatments of these knowledge categories and disciplines. Historically, Niitsitapi (Blackfoot) and other Indigenous worldviews have been systematically and systemically dismissed by the sciences and primarily confined to the humanities. Therefore, to move beyond merely learning about Indigenous cultures and lean into learning from them (i.e., learn through the lens of Indigenous cultures), we must (1) teach Niitsitapi and other Indigenous stories beyond the humanities classroom; (2) better engage the etiological, ontological, epistemological, axiological, and practical traditions of Niitsitapi and other Indigenous Peoples; and (3) operationalize pedagogical and methodological models that acknowledge, respect, and embody Indigenous ways of knowing and being in STEM education.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".