Politics of (Im)moderation: The Production of South Asian Identities in the Canadian Apology for Air India Flight 182
Bibliographic record
Abstract
Moderation, we are frequently told, is the key to a successful and fulfilling life: “everything in moderation,” and you will succeed, find happiness and become a well-adjusted member of society. The self-regulative function of “moderation”— whether in terms of one’s social life, nutritional habits or, in the case of this paper’s focus, political views and actions—has become a standard of capitalist Western citizenship and bourgeois political etiquette since Aristotle’s Nicomachean Ethics. As political theorist Wendy Brown notes in Regulating Aversion (2006), the polarization of the politically secular and liberal versus fundamentalist and radical has emerged as “part of a civilization discourse that identifies both tolerance and the tolerable with the West” (6). Bringing this analysis of social and political moderation to a Canadian context, I examine the use of the “moderate” in Stephen Harper’s 2010 apology for the Canadian government’s belated recognition of the bombing of Air India Flight 182 as a Canadian tragedy. This paper argues that in its supposed imperative of reconciliation, the Harper government does not question the racism against Indo- Canadians that has contributed to the dismissal of the bombing; instead, the government produces a binary between the radical, anachronistic terrorist and the “model minority” South Asian immigrant. The Harper apology not only makes any resistance to Canada’s “multicultural tolerance” irrational, but it also displaces all violent motives onto the foreign, South Asian body. The goal of this paper is to argue against the overt and systemic racism within Harper’s 2010 apology for Flight 182, and question the use of moderation in the political discourse of Canadian “diversity,” a discourse as innocuous and inclusive as it is deceitful, oppressive and even fatal.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.048 | 0.031 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".