Resisting a postcolonial construction of historical trauma and healing: Critical discourse analysis of public apologies in Canada
Bibliographic record
Abstract
The contemporary discourse around historical trauma and healing is site for debate and resistance in public spheres. Guided by critical scholars in language and power as well as post-and settler colonialism, this study analyzes texts and contexts of two public apologies in Canada – Chinese head tax, and residential schools for Indigenous children – to examine how historical trauma and healing were understood, and by doing so how the subject and object were re/constructed to maintain or resist social (dis)orders – postcolonial racial orders – in the past and the present of Canada. Findings included: (1) a split and temporal distance between the wrong past and the benevolent present with governments constructing themselves as the good subject reifying a sincere fiction of a liberal, benevolent, and just white-nation; (2) no acknowledgement of the cause of historical trauma, namely colonial governing; (3) ongoing construction of the other racialized population as victims/burdens/lesser citizens to current Canada; and (4) the explicit demand to collective forgetting of the past/historical trauma as current healing and inclusion. We discuss social responsibilities when historical wounds continue to leave injuries and the risk of perpetuating systemic violence to people with whom we currently share the nation all of us call home.
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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.046 | 0.063 |
| Scholarly communication | 0.017 | 0.005 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| 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".