Bibliographic record
Abstract
One way of contributing to the growing body of cultural criticism that considers the cultural politics of emerging ethnic canons in Canada is through an examination of the large body of critical attention paid to Joy Kogawa's Obasan . Obasan can be understood as a symptom of how Canadian literary studies has attempted to reinvent itself by trying to address a racial past in a multicultural present. Kogawa criticism should be at the centre of a contemporary rethinking of Canadian literary studies informing how critics have read and continue to read racialized texts and representations of histories of racism in Canada. It is also important to consider Asian American discussions of Obasan that are often informed by a tension between the cultural nationalist commitments of the Asian American movement and a concomitant desire to construct a coherent literary history with canonical texts.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.051 | 0.044 |
| Scholarly communication | 0.020 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".