Bibliographic record
Abstract
What do apologies apologize for? More precisely, what do the apologies regularly pronounced by states for some atrocity or other actually accomplish? This question animates my article. State apologies became an integral element of global political culture in the early 21st century. These politics of regret are reshaping Canadian national culture, most pronouncedly with the apologies for the Indian Residential School System ( CBC News 2008a ; McIntyre 2017 ) and the Komagata Maru ( CBC News 2008b ; Trudeau 2016 ). While Public Inquiries and Royal Commissions have long served as state responses to political mobilization, deployment of the machinery of regret has fast become the predictable response to accusations of atrocities, including genocide, enslavement and racial violence. Drawing on Frantz Fanon’s and Walter Benjamin’s ideas on violence, colonial in the case of Fanon (1961) , law in that of Benjamin (1996) , I examine the apologies delivered to Indigenous peoples and South-Asian diasporic communities by the Canadian state. Locating these pronouncements in the histories of violence they index, I demonstrate how such apologies function as techniques of violence that advance settler power structures and narratives of nationhood. My argument here is that apologies are themselves acts of violence which rework histories of brutalization to meet the political destabilizations of the present. Apologies thus reorganize the racial violence of settler societies, drawing sections of subjugated populations into waging this violence and, in the process, derail resurgent politics of decolonization, abolitionism and anti-racism.
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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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.027 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".