Teacher/Indigenous Partnerships: Building Engagement and Trust For History and Social Science Education
Bibliographic record
Abstract
What gets taught in publicly funded schools is a political choice.In Canada, these politics can clearly be seen with the release of the final report of the Truth and Reconciliation Commission (TRC), a government funded commission which examined the effects of Canada's abusive residential education system for Indigenous people from the 1870s through the 1990s.The TRC's Calls to Action, especially calls 62 and 63, advocates for the inclusion of Indigenous curriculum and content in publicly funded education systems.These calls have compelled provincial Ministries of Education, schoolboards, schools and teachers to have conversations about including Indigenous content and worldviews into their curriculum.Often missing from these conversations, especially in schools and school boards where the majority of the teachers are non-Indigenous, are the voices of Indigenous people themselves.By excluding Indigenous perspectives about content from curricular and pedological conversations, what is taught lacks a critical and decolonial lens and continues to support state agendas that privilege settler narratives over those of Indigenous nations. 2 This paper focuses on strategies non-Indigenous teachers can use to teach Indigenous content in ways that builds on the contributions Indigenous people have made to these conversations.In particular, formal teacher training is important, but so is self-education and reflection, and non-traditional approaches to content development and delivery, such as community partnerships.The important focus is not just on content that covers Indigenous peoples and history, but to cover this content in ways that align with Indigenous worldviews.My work as a non-Indigenous historian who has worked with Indigenous communities, teachers, and students to develop relevant Indigenous content and perspectives from the Ontario Social Science and Canada and World Studies curricula continues to demonstrate that unless there is 1 The author would like to thank Samantha Cutrara for her endless editing support, Meghan Cameron for helping me remember the work we did and always being a sounding board on educational ideas, and the Board of Directors of the GWCA, especially the education sub-committee (Meghan, Peter Farrugia, and Paula Whitlow) for taking the time to work with these education and programming ideas during our many meetings. 2For more on how state agendas are privileged in teaching, see Curry and Horn in this volume.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".