Troubling Settler Education in Ontario: The Challenges of Policy, Curriculum and Teachers
Bibliographic record
Abstract
Despite growing awareness of some of the assimilative practices, dispossession, violence, poverty, and lack of health and education services facing Indigenous peoples in Canada, many Canadians remain deeply ignorant of Canada’s past and present colonial systems. Indigenous and non-Indigenous leaders have identified this ignorance as a significant barrier to building post-colonial relationships. Curricula, policy, teacher education, and the preparedness of teachers to disrupt and reduce settler ignorance among non-Indigenous students through K-12 public education in Ontario is the focus of this dissertation. I review the curricular and teacher education reforms instituted in Ontario in response to the 2015 TRC Final Report, and through 56 semi-structured interviews, speak with teachers and teacher candidates directly about their knowledge of Indigenous and colonial topics and their attitudes toward implementing reforms in their classrooms. Teacher education reform has resulted in significantly more teacher candidates graduating with at least some training in Indigenous topics. Revisions to social studies and history curricula provide the opportunity for non-Indigenous Ontario students to learn more about Indigenous peoples and their history than ever before. My research, however, reveals that they will not learn much about themselves, their settler privilege, or the benefits they enjoy as Canadians at the expense of Indigenous peoples. Nine years after the TRC Calls to Action on education, and notwithstanding the work of curriculum writers, teacher educators, and many dedicated teachers across the province, Ontario K-12 education remains a deeply colonial space underpinned by a singular Western worldview that reinforces colonial logics and settler ignorance. Many teachers and graduating teacher candidates are ill-prepared to disrupt this. A much more robust effort is needed at every level of the education system to address a lack of basic knowledge about Indigenous topics, entrenched colonial thinking, and epistemologies of ignorance underlying Ontario education so that future generations of settlers can imagine and be motivated to work toward a shared decolonial future with Indigenous peoples.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.038 | 0.014 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| 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".