Traces and Residues of Migrant Boat Journeys: Reading the ‘MV Sun Sea’ and ‘Komagata Maru'
Bibliographic record
Abstract
Between 2009 and 2010, two Thai ships, the MV Sun Sea and Ocean Lady, brought 568 Tamil asylum seekers to Canada’s West Coast. Border authorities seized the ships and detained their passengers as security threats. For many criticizing this anti-migrant response, the arrivals of these ships echoed that of the Komagata Maru in 1914. This steamship entered the West Coast’s Vancouver harbour, but its 376 predominantly Sikh-Punjabi passengers were denied from disembarking as British subjects entering Canada. Scholarship on these incidents often use either the Komagata Maru as a lens for attending to the MV Sun Sea or vice versa. Part of the reason is that shortly after the government had apologized for its response to the Komagata Maru, it was detaining Tamil asylum seekers and arguing for their deportation. In suggesting their link far exceeds a temporal coincidence, this paper explores what makes it possible to think of the MV Sun Sea and Komagata Maru together. It argues that they are interlinked by an economy of affirmation and forgetting in Canadian public and political discourse. Furthermore, this economy frames how these boats are remembered unequally in service of the Canadian nation-state.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.023 | 0.022 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| 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".