Passing Recognition Obasan and the Borders of Asian American and Canadian Literary Criticism
Bibliographic record
Abstract
This article situates the reception of Joy Kogawa's Obasan within a comparative North American context, tracing the divergence and convergence of US and Canadian racial discourses in the canonization of Obasan in Asian American and Canadian literary studies. Through a consideration of how the novel both affirms the oppositional politics of Asian American literary studies and the fragmented nationalism of Canadian literary studies, this article argues that the politics of reception, appropriation and recognition that underwrite the text's cross-border canonization are prefigured by the text's own engagement with passing and recognition. While Obasan's blurring of Japanese Canadian and First Nations experiences can be read as a critique of Canadian racialization and the presumed universalization of US racial formations, such representational strategies are not immune to the colonizing gestures that inform the text's inclusion in Asian American and Canadian literary studies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".