Official Apology, Creative Remembrances, and Management of the Air India Tragedy
Bibliographic record
Abstract
On the 25 th anniversary of the Air India bombings, June 23, 2010, Prime Minister Stephen Harper delivered an apology at a commemorative ceremony in Toronto on behalf of the federal government to those who lost loved ones on Air India Flight 182. Yet even while pointing to an apparent crisis in multiculturalism, the text of the apology, which is analyzed here, does not put the Canadian state’s official multiculturalism policy into question. In seeking to offer redress, the official apology in effect functions as a tool of the state to manage the grief and grievance of racialized minorities, even as the state works toward increased surveillance of racialized minorities. Reading the apology in conjunction with two fictional remembrances of the Air India bombings, Bharati Mukherjee’s 1988 short story “The Management of Grief” and Anita Rau Badami’s 2006 novel Can You Hear the Nightbird Call? , this essay addresses the politics of official apology/official multiculturalism. Where the apology seeks to orient the Air India families away from dwelling in the past and toward the future, these fictional texts insist on opening up the past. Demonstrating the pressure on racialized minorities to civilly manage their grief and hide their grief, they trouble the state’s framing of the Air India tragedy as an exceptional or aberrant event in Canadian multiculturalism.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.022 | 0.044 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 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".