Treaty education for ethically engaged citizenship: Settler identities, historical consciousness and the need for reconciliation
Bibliographic record
Abstract
This article explores the possibilities of treaty education for reconciliation with First Nations people, as corrective to the foundational myth of Canada and as a means of fostering ethically engaged citizenship. Lack of historical understanding demonstrated by Canadians regarding treaties and the treaty relationship is examined in relation to discourses of liberal democratic citizenship. Drawing on ‘remembrance as a source of radical renewal’ ‘ethical relationality’ and ‘justice-oriented citizenship’, the argument is made that treaty education has the potential to help all students learn from and through events and experiences of the past in ways that inform not only their historical consciousness, but their dispositions as Canadian citizens, and their relationships with one another. While the discussion in this article is specific to treaty education, it is relevant to broader conversations about the role and value of including more diverse stories/experiences in national histories. Throughout the discussion, attention is paid to the interconnections of citizenship and history education, particularly with respect to possibilities for engaging differently in the world, alongside one another, politically, socially, culturally and ethically as part of the necessary and urgent process of 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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.043 | 0.087 |
| Scholarly communication | 0.017 | 0.009 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.004 | 0.008 |
| 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".