Reconciliation or Racialization? Contemporary Discourses about Residential Schools in the Canadian Prairies
Bibliographic record
Abstract
The residential school system is one of the darkest examples of Canada’s colonial policy. Education about the residential schools is believed to be the path to reconciliation; that is, the restoration of equality between Aboriginal and non-Aboriginal peoples in Canada. While the acquisition of the long-ignored history of residential schools has the potential to centre marginalized perspectives and narratives, knowledge acquisition alone is not necessarily a reconciliatory endeavour. The critical discourse analysis offered in this article reveals how dominant narratives about residential schools, cited by well-meaning educators, re-inscribe harmful colonial subjectivities about Aboriginal peoples. Through a post-structural lens and drawing from interviews conducted across one prairie province, I demonstrate how citing popular, contemporary discourses about residential schools continues to racialize Aboriginal peoples while positioning non-Aboriginal peoples as supportive and historically conscious. Readers are brought to think about how learning about residential schools for reconciliation might be approached as the disruption of subjectivities and the refusal to (re)pathologize Aboriginal peoples. Otherwise, efforts at reconciliation risk re-inscribing the racism that justified residential schools in their inception.
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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.050 | 0.074 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| 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".