Bibliographic record
Abstract
This essay explores a performance of commemoration of the “ Komagata Maru incident,” an event named for a ship that was the centre of controversy in 1914 when it arrived in the port of Vancouver bearing 376 would-be immigrants to Canada from British India. Most of the passengers on the ship were disallowed from entering Canada under three orders-in-council passed by the Dominion Government that amended Canada’s Immigration Act, as a part of a broader move against Asian immigration in the period. The essay focuses on a production developed and performed for the centenary of the arrival of the Komagata Maru out of a partnership between the Departments of Asian Studies and Theatre and Film at UBC; Rangmanch Punjabi Theatre , based in Surrey; and Srishti Institute for Art, Design and Technology in Bangalore, India. The production wove together selections from three Canadian theatrical representations of the event: “The Komagata Maru Incident” by Sharon Pollock (1976; in English), “The Komagata Maru” by Ajmer Rode (1984; in Punjabi), and “ Samuṅdarī sher nāl takkar ” or “Conflict with the Sea Lion,” co-authored by Sukhwant Hundal and Sadhu Binning (1989; in Punjabi). The plays, in conversation, act as a lens through which we can see how memory produces the present, and how the performed past creates possibilities for creative engagement with the present and future.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.023 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".