Liberal Regrets: A Cultural Study of Canada's Redress Politics
Bibliographic record
Abstract
This dissertation is an interdisciplinary study of the cultural and political struggles surrounding Canada’s attempts to legitimize its sovereignty through redress practices. Despite contestation, Canada’s redress politics, affectively mobilized through apologies, systematically silences significant feminist decolonial perspectives articulated in fiction by or on behalf of members of communities wronged by the state. Unsettling this silencing dynamic, this study de-centers the settler colonial logic of selected apologies through the lens of selected novels that narrate relevant wrongs differently. Drawing upon overlapping fields, including studies in the colonial nature of liberal democracy, state redress practices, and law and literature, Canada’s 1998 merged statement and 2008 apology to Indigenous peoples as well as three apologies (in 2007 and 2017) to five Canadian citizens tortured abroad after 9/11 are juxtaposed with selected novels by Lee Maracle and Sharon Bala. The rhetorical tactics in the 1998 merged statement and 2008 apology are analyzed as the affective dimension of Canada’s reconciliation discourse, framing the government’s settler colonial agenda for renewing its relationship with Indigenous peoples. I examine how Maracle’s novels - Sundogs, Ravensong, Daughters Are Forever, and Celia’s Song – deploy feminist decolonial narrative tools from Salish tradition that challenge anti-Indigenous violence perpetuated in these statements, situating them within Canada’s broader settler colonial project. Similarly, the rhetorical tactics in the 2007 apology to Maher Arar, 2017 apology to Abdullah Almalki, Ahmad El Maati, and Muayyed Nureddin, and 2017 apology to Omar Khadr are analyzed as efforts to legitimize state sovereignty by affectively reinforcing the gendered racial logic of Canada’s post-9/11 security discourse. I interpret Bala’s The Boat People, which depicts the plight of Sri Lankan Tamil migrant aboard the MV Sun Sea that arrived in Canada in 2010, as contesting the gendered racial logic of these apologies. Although the experiences of these migrants differ significantly from those of Arar, Almalki, El Maati, Nureddin, and Khadr, reading this novel as contesting the narrative of state sovereignty in the apologies to these citizens highlights the differential yet interrelated harms that Canada’s post-9/11 security discourse inflicts upon racialized citizens and non-citizens while situating this discourse within Canada’s ongoing settler colonialism.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.049 | 0.023 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".