Teaching and learning the legacy of residential schools for remembering and reconciliation in Canada
Bibliographic record
Abstract
In 2015, the Truth and Reconciliation Commission (TRC) of Canada released a Final Report containing 94 Calls to Action. Included were calls for reform in how history is taught in Canadian schools, so that students may learn to address such difficult topics in Canadian history as Indian Residential Schools, racism and cultural genocide. Operating somewhat in parallel to these reforms, social studies curricula across Canada have undergone substantial revisions. As a result, historical thinking is now firmly embedded within the curricula of most provinces and territories. Coupled with these developments are various academic debates regarding public pedagogy, difficult knowledge and student beliefs about Canada’s colonial past. Such debates require that researchers develop a better understanding of how knowledge related to Truth and Reconciliation is currently presented within Canadian classrooms, and how this may (or may not) relate to historical thinking. In this paper, I explore this debate as it relates to Indian Residential Schools. I then analyse a selection of classroom resources currently available in Canada for teaching about Truth and Reconciliation. In so doing, I consider how these relate to Peter Seixas’s six concepts of historical thinking (Seixas and Morton, 2013), as well as broader discussions within Canada about Indigenous world views, historical empathy and Reconciliation.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.046 | 0.021 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".