Métis Curricular Challenges and Possibilities: A Discussion Initiated by First Nations, Métis, and Inuit Education Policy in Ontario
Bibliographic record
Abstract
The Ontario Ministry of Education’s (2007) Ontario First Nation, Métis and Inuit Education Policy Framework asks school boards to “provide a curriculum that facilitates learning about contemporary and traditional First Nation, Métis, and Inuit cultures, histories, and perspectives among all students, and that also contributes to the education of school board staff … [and] teachers” (p. 7). The framework is a conduit to push First Nation, Métis, and Inuit initiatives beyond the exceptional program or course found in a few Ontario schools. However, in our recent Report on Métis education in Ontario’s K-12 schools (2012) for the Métis Nation of Ontario’s Education and Training Branch, we found only a small portion of school boards were engaged actively in bringing the above mandate to life. As to Métis, the challenge is largely a lack of awareness of Métis history and culture. Our findings show there is a need for more Métis curricular material to be developed to broaden the appreciation, awareness, and understanding of the historical and contemporary Métis. Here, we share curricular challenges and possibilities in heading the call from Métis for a nuanced portrayal of families and communities at school.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.021 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.051 | 0.025 |
| Scholarly communication | 0.016 | 0.007 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".