Undermining Justice: The Political Framing of Actors in the Independent Assessment Process
Bibliographic record
Abstract
This article presents findings from a critical discourse analysis of House of Commons debates about the Independent Assessment Process (IAP), an out-of-court compensatory adjudication process intended to resolve claims of sexual and physical abuse that occurred at Indian Residential Schools and one of five key elements of the Indian Residential School Settlement Agreement. Our analysis is guided by the question: What do elected officials’ discussions about the IAP reveal about the implementation of compensatory transitional justice mechanisms in settler colonial states, and about colonial relations (specifically attempts at reconciliation) more generally? Our study focuses on debates that took place between 2004 and 2019. We explored elected officials’ framing of both Survivors and the Canadian State in their discussions about the IAP. Our analysis reveals the limited reach of dialogue based in a partisan and antagonistic context and supports those scholars who assert that transitional justice is incompatible with reconciliation and decolonization. By way of contributing to the larger interdisciplinary study entitled Reconciling Perspectives and Building Public Memory: Learning from the Independent Assessment Process, of which this article is part, we reflect on what our findings mean not only for public memory but also for studying the IAP moving forward.
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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 | 0.054 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.056 | 0.148 |
| Scholarly communication | 0.028 | 0.018 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 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".