THE TRANSFORMATIVE POTENTIAL OF THE TRUTH AND RECONCILIATION COMMISSION: A SKEPTIC’S PERSPECTIVE
Bibliographic record
Abstract
From 2009 to 2014, Canada’s Truth and Reconciliation Commission (TRC) heard the testimony of Indigenous persons who had once been students in Indian Residential Schools and held events to permit discussions with the Canadian public about the impact and consequences of the historic effort to assimilate Indigenous individuals and erase their cultures. In doing so, the TRC performed its mandate with sensitivity and skill. This article focuses on the final report of the TRC that, the author argues, fails to focus sufficiently on the means by which Canadian law and policy continues to deny Indigenous peoples the power to assure their own welfare. The project of reconciliation with Indigenous peoples was not aided by this failure to communicate clearly the extent to which the colonial attitudes that led to the residential schools policy remains firmly anchored in the structures of the Canadian state. Through an examination of the basic elements of the concept of reconciliation, including reciprocal engagement and relational change, this paper concludes that a different approach will be required to mobilize the transformation implicit in meaningful reconciliation.
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 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.022 | 0.063 |
| Scholarly communication | 0.023 | 0.008 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.012 | 0.016 |
| Insufficient payload (model declined to judge) | 0.002 | 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".