Beyond smell and sensationalism: remixing durian for and by Asians and Asian Canadians in Canada
Bibliographic record
Abstract
Increased imports of durian to Canada has reiterated what Wenying Xu identifies as the tension between dominant food sophistication and ‘crude’ immigrant practices, where denigrating immigrant foodways was integral to colonial assimilationist projects (2007). While fresh durian in Canada appears in sensationalist online media that associate durian with disgust, it is also made palatable and popularized by mainstream non-Asian food bloggers and chefs. Focusing on online food discourse, this article examines the fraught position of durian in dominant Canadian foodways as associated with disgust when imported for Asian consumers, and with intrigue and exoticism when marketed in ‘elevated’ dishes created or consumed by white Canadians. It proposes that durian has nevertheless developed into a diasporic cultural connector and site of community building within Asian and Asian Canadian creators and consumers that resists this Orientalization, linking durian’s emergence in Canada to a larger history of Asian food ingenuity and adaptation to global marketplaces. The ’remixing’ of durian by Asian chefs who combine Asian and Asian Canadian cuisines, and diasporic connections created over discussing durian in online spaces, contest xenophobic and racist sentiments focused on Asian foodways, positioning online responses to durian as an emerging yet significant site of community and resistance.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.031 | 0.013 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".