In Our Own Words: Asian Mixed-Race Identity in Contemporary Canadian Literature
Bibliographic record
Abstract
This dissertation examines Asian mixed-race identity in twenty-first-century Canadian literature. Mixed-race people have been subject to two persistent stereotypes: historically, they were pathologized as perpetually confused about their racial identity, but more recently, multiracial people are celebrated as the embodiment of a post-racial future. Although critical mixed-race studies is an expanding field, critical study of Asian mixed-race literature in Canada remains scarce. My dissertation addresses this research gap by focusing on recent literature written by Asian mixed-race authors who illuminate the complex processes of multiracial identity development in their texts and reject stereotyping. I trace commonalities in the discussion of mixed-race identity development in Tessa McWatt’s, David Chariandy’s, Avan Jogia’s, Leanne Dunic’s, Kyo Maclear’s, Saleema Nawaz’s, William Ping’s, and Jia Qing Wilson-Yang’s texts and the new possibilities they present for multiracial identity expression. The memoirs, multimedia projects, and novels present a range of multiracial experiences but share a common understanding that mixed-race identity is two things: personal and malleable. As I argue, multiracial identity is personally constructed, situationally adaptable, and in flux throughout the mixed-race person’s life. This dissertation thus articulates how contemporary Asian mixed-race literature in Canada enriches and complicates the field of critical mixed-race studies, which has been preoccupied with American race relations. Furthermore, these texts contribute to Asian Canadian literary studies in that they engage with the tension between Canada’s past racist treatment of Asian people and respond to the more recent racist construction of Asian people as the “model minority” from a white settler perspective. Ultimately, my discussion shows how Asian multiracial literature resists the constraints of national myths that portray Canada as a post-colonial, racially harmonious nation and reveals that complex racial dynamics continue to affect personal and national identity.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.045 | 0.019 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".