“War-on-Terror” Frames of Remembrance: The 1985 Air India Bombings After 9/11
Bibliographic record
Abstract
This paper critically analyzes Canadian filmmaker Sturla Gunnarsson’s documentary Air India 182 in light of recent official efforts to remember and redress the 1985 Air India bombings. The author argues that the film, in line with official efforts, constructs a narrative of the bombings through a “war on terror” framing of remembrance that is at once specific to the recircuitries of race produced in the anxious aftermath of 9/11, and consistent with historically rooted operations of xenophobia and colonial power. The significance of such a framing is that it works not only to shape memory of the bombings as a certain kind of event (one with unambiguous perpetrators, victims and damages), it narrows the field of what are imagined as possible actions toward redressing or compensating for its losses. In other words, a war-on-terror framing of remembrance, as a discursive strategy or approach to “remembering” the bombings, limits the potential for a complex understanding of the politics out of which this event arose, restricting public debate over the kinds of responses that continue to be generated in its aftermath. Moreover, a war-on-terror framing of remembrance is understood here to employ neoliberal and settler-colonialist discourses of productive futurity and multicultural tolerance to make remembrance of the bombings concomitant with the construction of turbaned Sikhs and other racially and religiously minoritized citizens as “dangerous internal foreigners.” As such, this paper bears implications beyond the documentary film, including the consequences of neoliberalism for the formation of public memory and for the making of race and nation in Canada.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.018 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| 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".