“Fearing your own queer self”: Depictions of Diasporic Queer Experience in Grace Lau’s Poetry
Bibliographic record
Abstract
The intersection of migrant and queer experiences constitutes one of the core motifs of The Language We Were Never Taught to Speak (2021), the debut poetry collec- tion by Grace Lau, a Chinese Canadian poet. Through a series of interconnected vignettes, Lau provides an insight into her experiences as both a Canadian and a Chinese immigrant, a lesbian and a failed model child, an aficionado of traditional Chinese culture and an en- thusiast of contemporary Western popular culture. The mosaic of experiences illustrates the complexity and intricacy of the author’s identity/ies. Through the analysis of three poems (“The Levity,” “The Lies That Bind,” and “My Grief Is a Winter”), supported with references to the theoretical works on Asian North American writing and queer Asian mi- grant experience, the article discusses Lau’s depictions of queerness and her experiences as a Chinese immigrant in relation to the Canadian LGBTQ+ community, white queer liberalism, and internal politics of the Chinese diaspora. It proposes to see Lau’s poetry as an example of biomythography, a form of autobiographical writing showcasing how encounters with different communities shape the subject. In the process of disentangling her complex ties with the Chinese diaspora, the white Canadian LGBTQ+ community and her own family, Lau reveals the impact of her interactions with those different groups as she can finally express her identity as a queer Chinese Canadian.
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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 | 0.001 | 0.003 |
| 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 | 0.019 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".