Locating a Theoretical Framework for the Canadian Truth and Reconciliation Commission: Charles Taylor or Nancy Fraser?
Bibliographic record
Abstract
The Truth and Reconciliation Commission (TRC) of Canada was established to uncover and acknowledge the injustices that took place in Indian residential schools and, in doing so, to pave the way to reconciliation. However, the TRC does not define reconciliation or how we would know it when (and if) we get there, thus stirring a debate about what it could mean. This article examines two theories that may potentially be relevant to the TRC’s work: Charles Taylor’s theory of recognition and Nancy Fraser’s tripartite theory of justice. The goal is to discover what each theory contributes to our understanding of the harms that Indigenous peoples suffered in residential schools, as well as in the broader colonial project, and how to address these harms appropriately.
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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 | 0.018 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.035 | 0.111 |
| Scholarly communication | 0.023 | 0.013 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.012 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 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".