Incorporating Culture in the Curriculum: The Concept of Probability in Nunavik Inuit Culture
Bibliographic record
Abstract
Traditionally, Canadian Inuit have lived in the circumpolar regions of Canada and those who still live in these regions, have their own cultures, which they tend to celebrate in their educational curricula. Inuit culture reflects their traditional lifestyle, when they were nomadic, and hunted and fished to survive in incredibly difficult conditions. These cultural differences present many challenges and issues to some mathematical concepts; for instance, for Nunavik Inuit, the concept of probability has no formal definition and it does not take the same meaning as in conventional mathematics. This misalignment could cause negative effects on students’ learning. Looking to bridge the gap between those two different cultural meanings, the principal investigator, Annie Savard, with the assistance of Inuit educators designed learning situations based on the traditional Inuit culture. We used an ethnomathematical model (Savard, 2008b) to frame the learning situations created. In this article, we present the learning situations created that aimed to bridge Nunavik Inuit culture and the development of probabilistic reasoning and we discuss how these learning situations supported students’ mathematical understanding and cultural identity.Keywords: Nunavik Inuit traditional culture; probability; learning situationethnomathematical model
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.012 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".