Teaching Treaties as (Un)Usual Narratives: Disrupting the Curricular Commonsense
Bibliographic record
Abstract
This article examines the importance of treaty education for students living in a province entirely ceded through treaty. Specifically, we ask and attempt to answer the questions “Why teach treaties?” and “What is the effect of teaching treaties?” We build on research that explores teachers’ use of a treaty resource kit, commissioned by the Office of the Treaty Commissioner in Saskatchewan. Working with six classrooms representing a mix of rural, urban and First Nations settings, the research attempts to make sense of what students understand, know and feel about treaties, about First Nations peoples and about the relationships between First Nations and non–First Nations peoples in Saskatchewan. It is revealing that initially students are unable to make sense of their province through the lens of treaty given the commonsense story of settlement they learn through mandated curricula. We offer a critique of the curricular approach in Saskatchewan which separates social studies, history and native studies into discrete courses. Drawing on critical race theory, particularly Joyce King’s notion of “dysconscious” racism, we deconstruct curriculum and its role in maintaining dominance and privilege. We use the term (un)usual narrative to describe the potential of treaty education to disrupt the commonsense. (Un)usual narratives operate as both productive and interrogative, helping students to see “new” stories, and make “new” sense of their province through the lens of treaty.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.019 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.002 | 0.005 |
| 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".