Framing the Truth and Reconciliation Commission Process in Canada: A Media Analysis of Settler Colonial Rhetoric and Colonial Denial, 2003-2016
Bibliographic record
Abstract
The Truth and Reconciliation Commission (TRC) of Canada helped to expose the trauma experienced by Indigenous peoples in Canada’s Indian Residential Schools (IRS), which were governed and run by government and church officials. In 2008, the Canadian government formally apologized for these residential schools. This apology was undermined, however, by a denial of colonial history by Canada at the G20 in 2009, revealing a rhetorical contradiction that is part of a public narrative of colonial denial. This paper examines the public discourse during and after the TRC process to understand the impact of negative discourses regarding the TRC and colonialism. This case study examines written content from five Canadian media platforms that covered the Alternative Dispute Resolution (ADR) and TRC process between 2003 and 2016. Drawing on concepts such as the white possessive, white rage, and white fragility, the aim of this paper is to unpack the cognitive dissonance of apology concurrent with the rhetoric of settler colonial denial. Findings from the discourse analysis substantiate the hypothesis that continued dominant narratives of settler colonialism align with representations of the TRC process. This limits the authentic potential for a formal apology to address the IRS legacy which perpetuates continued settler colonial realities in Canada.
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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.027 | 0.019 |
| Scholarly communication | 0.016 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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".