Bibliographic record
Abstract
After her acclaimed debut novel Ru (2009), which poetically narrates a personal journey of a Vietnamese boat person growing up in Montréal, Kim Thúy became a familiar name within the Québécois literary scene. Much of her success was the result of a casual yet effective writing style that blended trauma writing with a refugee sentimentality, which consistently moves between themes of integration and alienation. Her follow-up novel, Mãn , was no different in that it continues the same balancing act between cultural and linguistic coherency and absolute aporias. In this chapter, I argue that in Mãn , rather than producing a field of intelligibility to bridge the gap between the traumatic experience of childhood and the Vietnam War, Thúy uses the figure of untranslatability to challenge our understanding of cultural alienation. This takes place on three different levels: first is the personal, where one must write the self; second, the landscape of refugee writing; and last, the broader landscape of Asian-North American literature. Taking Naoki Sakai’s explication of the figure of translation as an operation of co-figuration, wherein the unity of language and culture are produced within the act of translation itself, I read moments of cultural and linguistic translation within Mãn as violent non-encounters with the putative county of Vietnam, and as a demonstration of the aporia of translation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.030 | 0.008 |
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".