BEYOND RECOVERY: HEALING AND CANADA’S TRUTH AND RECONCILIATION COMMISSION
Bibliographic record
Abstract
This thesis explores the concept of healing used by Canada’s Truth and Reconciliation Commission and survivors as a conceptual tool to address and redress the legacy of residential schools. Using public testimony and selected interviews, I explore how the TRC’s statement-gathering process is perceived and experienced by survivors. This thesis also documents the personal tensions and political limits encountered during the implementation of a globalized, institutional process of truth-telling applied to resolve diverse and localized ‘traumas’ experienced by students enrolled in dozens of residential schools. This approach illustrates the inherent shortcomings of a top-down approach to solving residential school issues, drawing on the public testimonies of survivors to identify tensions between a national process and survivor-led and community-based alternatives for healing. Despite its intention to create a forum that allows survivors to tell their story about residential schools, the TRC has also, often, been used as space of political activism and social critique. Survivors have used the public testimonial spaces offered by the TRC to both critique the Canadian government’s commitment to reconciliation and also to demand more effective forms of redress, which have subtly shaped and transformed the TRC during its mandate. Thus, while I draw attention to institutional practices, ideologies and power relations shaping the TRC, I also emphasize how people perceive, engage and transform the process as a result.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.034 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.066 | 0.049 |
| Scholarly communication | 0.035 | 0.011 |
| Open science | 0.007 | 0.016 |
| Research integrity | 0.047 | 0.054 |
| Insufficient payload (model declined to judge) | 0.019 | 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".