Bibliographic record
Abstract
When Canadian Theatre Review published its special issue on Chinese Canadian Theatre in 2002, Jean Yoon opened her leadoff article with “Gee, you know, I’m not Chinese. I’m Korean.” Situating herself within “an Asian Canadian arts scene – one that is fluid, vibrant, and inclusive” (emphasis in original), Yoon criticized CTR for focusing so narrowly on a single, atomized community in Canada, potentially ghettoizing it and fragmenting fragile intercultural solidarities within a no longer first-generation Asian Canadian community that neither remembered nor cared about ancient histories of tension between the homelands. She called for a look, first, at “the bigger picture” (5). Since then, one play has been produced in Toronto that focuses on the potentially divisive topic of the Nanking Holocaust of the Chinese by the Japanese in 1937 (Marjorie Chan’s a nanking winter), and another (Diana Tso’s Red Snow) is in development. Seven years later, neither seems controversial or threatens solidarities within an Asian Canadian theatrical community that is now, perhaps not incidentally, well represented through the efforts of fu-GEN Asian Canadian Theatre Company, founded in 2002 – the year Yoon’s article appeared.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.114 | 0.029 |
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".