Unsettling our Narrative Encounters within and outside of Canadian Social Studies
Bibliographic record
Abstract
In 2007, Indian Residential School System (IRS) survivors won a class action settlement worth an estimated 2 billion dollars from the Canadian Government. The settlement also included the establishment a Truth and Reconciliation Commission. Despite the public acknowledgement, we posit that there is still a lack of opportunity and the necessary historical knowledge to address the intergenerational impacts of the IRS system in Ontario's social studies classrooms. In this essay we therefore ask: How might we learn to reread and rewrite the individual and collective narratives that constitute Canadian history? In response to such curriculum inquiries, we lean upon the work of Roger Simon to reread and rewrite historical narratives as shadow texts. For us, life writing as shadow texts, as currere, enables us to revisit the past as a practice of unsettling the present, toward reimagining more hopeful future relations between Aboriginal and non-Aboriginal communities across the territories we now call Canada. As Simon's life-long scholarly commitments make clear in this essay, the onus lies with those present to teach against the grain so that we might encounter each other's unsettling historical traumas with compassion, knowledge, and justice.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.073 | 0.074 |
| Scholarly communication | 0.017 | 0.006 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.006 |
| 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".