Settling Accounts at the End of History: A Nonideal Approach to State Apologies
Bibliographic record
Abstract
What are we to make of the fact that world leaders, such as Canada’s Justin Trudeau, have, within the last few decades, offered official apologies for a whole host of past injustices? Scholars have largely dealt with this phenomenon as a moral question, seeing in these expressions of contrition a radical disruption of contemporary neoliberal individualism, a promise of a more humane world. Focusing on Canadian apology politics, this essay instead proposes a nonideal approach to state apologies, sidestepping questions of what they ought to do and focusing instead on their actual functioning as political acts. Through a sociologically informed speech act theory and Foucault’s work on power, apology is conceptualized as a speech act with an essentially relational nature. The state, through apologizing, reaffirms the norms governing its relationship to its subjects at a moment when a past transgression threatens to destabilize this relation. From a Foucauldian point of view, the state’s power inheres in the very stability of the state–citizen relation, and we should therefore see apologies as defensive moves to protect state hegemony. In the context of Western liberal democracies, such as Canada, apologies embody, rather than challenge, the logic of neoliberal governmentality by suggesting that everything, including resentment against the state, can be managed within the current status quo. Nevertheless, total cynicism about apology politics is not warranted. In many indigenous apology campaigners’ demands for contrition we see another side of apologies: their potential to bring about change by enacting counterhegemonic relations to the state.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.018 | 0.079 |
| Scholarly communication | 0.020 | 0.014 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".