Incorporating Indigenous Content Into K-12 Curriculum: Supports for Teachers in Provincial and Territorial Policy and Post-Secondary Education Spaces
Bibliographic record
Abstract
In an era of learning truth and working towards reconciliation with Indigenous peoples, education institutions across Canada are in the midst of decolonizing their education spaces. Fundamental to this process are the Truth and Reconciliation Commission Calls to Action to educate settler teacher candidates to develop culturally appropriate curricula and incorporate Indigenous content into their teaching practices. Little research has reviewed institutional responses to these recommendations. To fill this gap, this study compiles recent efforts to inform Ministries of Education and post-secondary education institutions of effective and culturally safe methods to incorporate Indigenous content in curricula, based on current interventions and the lived experiences of teachers navigating the decolonization process. Two rapid reviews of grey and academic literature are completed. The findings shed light onto course-, professional workshop-, and policy-based interventions to support teachers in teaching Indigenous content. Interventions often prioritize cultural safety to underline teaching practices and focus on addressing settler biases, racism, and harmful stereotypes. Many Bachelor of Education programs offer mandatory courses on how to infuse Indigenous worldviews into curriculum, and emphasize building relationships, challenging positionalities, and establishing safe spaces to ask questions. Many teachers benefit from cultural safety training and resources, however, some continue to face challenges in confronting their roles and responsibilities as settlers within education spaces. As decolonizing education is an ongoing process, this research aims to provide key information to advance its progress. To that end, future research needs to investigate the long-term impacts of existing interventions on teaching practices and curriculum development.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".