A Lesser Injustice: Breaking with the Past and Contesting Memory through the Indian Residential School Settlement Agreement, 2005-2017
Bibliographic record
Abstract
This thesis considers the Indian Residential School Settlement Agreement in Canada (the “Settlement Agreement”) through the lens of Memory Studies, History, and Critical Indigenous Studies. Through an analysis and deconstruction of the Settlement Agreement and its various official implementation processes (the Independent Assessment Process, the Common Experience Payments, and the Truth and Reconciliation Commission) this work finds that the Settlement Agreement represents a site of contested memory, in that the official federal narratives surrounding this contract greatly contrast the lived experiences of the Indian Residential School System survivors who entered into this agreement. Furthermore, this work argues that Canada’s federal government and churches have used the Settlement Agreement to attempt to maintain colonial power, to control the historical narrative and national memory of the Indian Residential School System, and to “break with the past” by suggesting that this agreement has resolved Canada’s residential school legacy, and absolved its perpetrators. In turn, residential school survivors have resisted and rejected these politics of recognition and “break with the past” rhetoric through every Settlement Agreement process, and continue to heal from the Indian Residential School System Legacy through radical sovereignty, ceremony, and self-recognition.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.036 | 0.018 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".