Interweaving Iiyiyiu (Cree) Ways of Knowing and Land-Based Learning with Science Curriculum at Waapinichikush Elementary School, Chisasibi, Quebec
Bibliographic record
Abstract
This study is the result of work done in close collaboration and partnership with local Cree (Iiyiyiu) teachers, Elders and community members at Waapinichikush Elementary School in Chisasibi, Quebec, to identify approaches to learning and teaching science that are culturally appropriate and to bring student learning outdoors and onto the land. A participatory action research approach incorporating Indigenous research methods was used. Teachers, Elders and other community members worked collaboratively in developing land-based science learning opportunities. Qualitative observations were then used to determine if students were more engaged in land-based learning, had a greater sense of cultural identity, and achieved greater academic success including an increased use of the of Cree language (Iiyiyiuyimuwin) with these methods of teaching. The aim of this research is to work towards a decolonized approach to teaching and learning by building bridges between Western and Iiyiyiu approaches to education and especially, to prioritize and emphasize Iiyiyiu ways of knowing in leading land-based initiatives at Waapinichikush Elementary School. This project also identified several practical ways for non-Indigenous teachers to respectfully adapt their lessons to seasonal themes based on Cree (Iiyiyiu) perspectives and land-based learning. By prioritizing Iiyiyiu ways of knowing and land-based approaches to learning, it is hoped that a more decolonized vision of education will emerge especially in the teaching of science. Students who participated in outdoor learning opportunities were observed to be highly engaged and enthusiastic and showed increased knowledge of Cree vocabulary. Students were also more focussed on academic tasks when we returned to the classroom after participating in land-based activities. Community and parental involvement gradually increased as this study progressed, and positive involvement and interest emerged. Finding a balance between teaching the Western standards of education, especially in the teaching of classroom science and the cultural knowledge and teachings from the land remains an ongoing challenge. However, this study demonstrated the interest, capacity and expertise that exists in the community and shows great promise for the development of future initiatives at the school which could build upon the resources and expertise identified.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".