Making Chemistry Relevant to Indigenous Peoples: An Inuit Case Study
Bibliographic record
Abstract
The ability of our northern Indigenous peoples (Inuit, Iñupiaq, and Yupik) to survive and thrive in the Arctic depends significantly upon underlying chemistry and chemical principles. Here, we explore four of these connections and then show how the Indigenous experience can be incorporated into science and chemistry courses. To accomplish our goals, we have knitted together the Indigenous experimental knowledge and cultural background of two Inuit science students with the depth and breadth of chemistry knowledge of a teaching-focused chemistry professor. Their combined investigations resulted in a series of published articles explaining the chemistry underpinning many aspects of Inuit life in the Arctic. Then we provide commentaries of the experiences of two high school science teachers who have incorporated this work into their chemistry and science classes in very different teaching environments. We contend that incorporating contextualized Indigenous content is important for two main reasons. Making chemistry more relevant for Indigenous students will spark their interest in the subject, make them feel valued, and possibly proceed to further science studies. Incorporating Indigenous-relevant chemistry for the wider population of students will enable them to appreciate the sophistication of an Indigenous culture and add an additional dimension to their chemistry studies.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.031 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".