First Nation, Métis, and Inuit Education Leads: Transforming Education by Sharing Our Praxis
Bibliographic record
Abstract
In the fall of 2016, the Ontario Ministry of Education (Ministry of Education [MOE], Indigenous Education, 2016) announced that each school board was required to have a dedicated position under the umbrella title First Nation, Metis, and Inuit Education (henceforth referred to as the Lead). The MOE also provided the funding for this position. This new funding and mandate ensured that all school boards had the capability to create a new position and/or continue supporting their current Lead position(s). However, the MOE provided few guidelines for what this work should entail, and they offered no mandatory training to the Leads. Therefore, in the absence of substantial directions from the MOE, it is critical that these Leads, academics, and other people that work in the field of Indigenous education communicate about the possibilities of this work. This paper is a small contribution to this subject area, in hopes that it will create a much-needed conversation about the future of Indigenous education in elementary and secondary schools. This paper will begin by theorizing about some of the difficulties and barriers that some Leads may experience. Then it will offer one strategy that one school board is using to implement Indigenous education in Ontario.
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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.037 | 0.028 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".