Broken Passages and Broken Promises: Reconstructing the Komagata Maru and Air India Cases
Bibliographic record
Abstract
My dissertation examines two events in Canada’s past that have played formative roles in the debate about the place of the South Asian diaspora within the Canadian nation. The first is the 1914 Komagata Maru incident, in which 352 British subjects of South Asian origin aboard a Japanese ship – the Komagata Maru – were denied entry into Canada and forced to return to India. The second is the 1985 bombing of Air India Flight 182, an event that claimed the lives of almost 300 Canadian citizens, most of South Asian origin, who were traveling from Canada to India. My dissertation reads literary and cinematic reconstructions of the Komagata Maru and Air India cases as crucial sites of healing as well as archives in which the historical memories of diasporic groups are recorded. Drawing on but also extending the work of Benedict Anderson who argues that nations are imagined communities formed by both remembering and forgetting, I suggest that works of fiction can counteract the nation’s tendency to forget. In this specific instance, I argue that certain kinds of fiction can prevent the Canadian nation from “forgetting” the Komagata Maru and Air India cases and, in so doing, can contribute to the project of shaping the nation in more inclusive ways by insisting that certain acts, with all the consequences that followed from those acts, did take place.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.039 | 0.041 |
| Scholarly communication | 0.015 | 0.006 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".