The Structural Injustice Turn, the Historical Justice Dilemma and Assigning Responsibility with the Canadian TRC Report
Bibliographic record
Abstract
Abstract This article addresses the historical justice dilemma: although critical memory is indispensable for accountability, efforts to use it are often hampered by the unjust relations and systems that caused the wrongs to which historical justice is compelled to respond in the first place. Contemporary authors tackle this problem by focusing on collective responsibility for structural injustice. This article takes a different tack. Studying closely the 2009–2015 Truth and Reconciliation Commission of Canada (TRC) report, it argues that the structural turn may come at the expense of a focus on agency and may thus provide unwitting anonymity for wrongdoers while crimping our thinking about leadership and responsibility. Although this article strongly criticizes the TRC report, it tries to work constructively with it, developing an analysis that compensates for the report's unwitting invisibilization of perpetrators. Distilling portraits and analyses of wrongdoer agency that are latent in the TRC's postwar history volume, this article shows how we can develop the report as a resource of what I call retributive social accountability.
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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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.036 | 0.047 |
| Scholarly communication | 0.015 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.009 |
| 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".