The Importance of Remembering in Relation: Juxtaposing the Air India and <i>Komagata Maru</i> Disasters
Bibliographic record
Abstract
In 1914, a ship named the Komagata Maru arrived off the coast of British Columbia carrying 376 would-be immigrants, mostly Sikh men from India desiring to settle permanently in the burgeoning colony of Canada. A public sentiment of concern about “Hindoo Invaders” and a discriminatory immigration policy caused the ship to be anchored for two months in the Vancouver harbour, during which time passengers were occasionally denied food and water. The ship was finally forced back to sea under threat from the weaponry of a Canadian navy vessel. At a first glance, this event seems to have little in common with the 1985 bombing of Air India Flight 182, a terrorist act commonly believed to have been committed by Sikh separatists fighting for an independent Khalistan, or Sikh homeland, in India. Yet in Anita Rau Badami’s novel Can You Hear The Nightbird Call? and Uma Parameswaran’s poem “On the Shores of the Irish Sea,” the Komagata Maru and Air India disasters are explicitly linked. In this paper, I explore the reasons these authors might want to encourage a wider public to remember these two events in relation. How might remembrance of one refract and reframe memories of the other? Ultimately, I turn to recent state-sponsored efforts to memorialize and offer reconciliation for the Air India and Komagata Maru atrocities to demonstrate how failing to remember these events in relation makes it is easier to cloak the overt racism underpinning both and to thus maintain the veneer of “Canada” as a haven for racial diversity. By contrast, if public memorials of one event were designed in such a way as to explicitly make links to the other, I argue, a wider public might come to recognize how the unresolved injustice of the Maru incident is deeply implicated in the cascading torrent of violent atrocities that contributed to the bombing of Air India Flight 182, a connection virtually impossible to draw from current state-sponsored efforts to memorialize either event.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.022 |
| Scholarly communication | 0.015 | 0.017 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.006 |
| 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".