Food, Family and Chineseness: Finding Belonging in Carrianne Leung’s The Wondrous Woo
Bibliographic record
Abstract
“Straddling what they often describe as two cultures,” second generation and 1.5 generation children of Chinese immigrants report feeling “never fully at home” in Canada (Kobayashi and Preston 236). Disconnected from their Chinese roots and rejected by the Canadian majority population, the Woo children struggle with this feeling of in-betweenness in the novel The Wondrous Woo. Carrianne Leung constructs a narrative of finding belonging through the different dishes that the Woo family creates, consumes, and encounters. Looking at food as a cultural marker and as a means of establishing identity and community, this presentation will examine the Woo children’s attempts to feel at home, including trying to efface their Chineseness to fit in to dominant Canadian society. From Ba’s summer barbeques to Miramar’s cooking when attending the University of Ottawa, the novel criticizes this problematic process of achieving belonging through assimilation. Instead, the narrative arrives at the solution of family and togetherness: the Chinese-Canadian diasporic community must establish its own place by reconnecting with Chinese culture, and “in food lies this hope” (Leung 97).
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.032 | 0.025 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| 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".