Are we there yet? The road to reconciliation in Ontario public school curricula since 2007
Bibliographic record
Abstract
This research is a multi-layered examination of existing policy frameworks and curricula surrounding Indigenous education in Ontario public elementary schools: how these documents have evolved and been adapted within the Ontario public elementary school system, focusing on the years since the turn of the 21st century. Since 2007, the Ontario Ministry of Education has taken a new approach to Indigenous education within public school classrooms. They have certainly made strides towards providing an equitable workspace for Indigenous students within mainstream classrooms and curricula, but have policy makers and educators alike gone far enough? Does the existing framework adequately provide for the recommendations of the Truth and Reconciliation Commission for teaching and learning by upholding Indigenous world views and knowledge systems within the constraints of colonial curricula and pedagogy? Or do these provisions simply mask a modern and more covert form of assimilation within the public education system? If this is so, why should we, and how can we, provide a more authentic Indigenous educational experience within Ontario’s public elementary school classrooms?
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 | 0.016 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.032 | 0.025 |
| Scholarly communication | 0.016 | 0.008 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".