The Ethics of Reconciling: Learning from Canada’s Truth and Reconciliation Commission
Bibliographic record
Abstract
In 2008, the Truth and Reconciliation Commission of Canada (TRC) was initiated to address the historical and contemporary injustices and impacts of Indian Residential Schools. Of the many goals of the TRC, I focus on reconciliation and how the TRC aims to promote this through public education and engagement. To explore this, I consider two questions: Ethical queries arise which speak to broader concerns about the TRC’s capability to fulfill its public education goals. I raise several concerns about whether the TRC’s plan to convoke the collective will result in over-simplifying the process by relying on blunt, poorly defined identity categories that erase the heterogeneity of those residing in Canada, as well as the complexity of the conflict among us. I attempt to situate myself in-between proclamations of “success” or “failure” of the TRC, to better understand what can be learned from contested truths and experiences of uncertainty.
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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 | 0.024 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.054 | 0.091 |
| Scholarly communication | 0.026 | 0.011 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.010 | 0.016 |
| Insufficient payload (model declined to judge) | 0.004 | 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".