Bibliographic record
Abstract
Narratives of culinary tourism describe the experience of travel undertaken with the express purpose of retracing foodways, and cooking and eating in a cuisine’s place of origin. This article explores culinary travelogues by Jen Lin-Liu, Anne Mah, and Cheryl Lu-Lien Tan. In each narrative, Asian American women travel to Asia to learn about food and, by doing so, reconnect with familial and cultural pasts about which they feel ignorant. These texts address a mainstream American readership and construct a sense of race-neutral intimacy through avowals of ignorance about Asia and by offering aspirational narratives of professional resolve and domestic and romantic satisfaction. I argue that the texts’ authors are nevertheless anxious that their relationship with readers could be imperiled by the taint of otherness historically ascribed to Asian food and, by extension, to Asian bodies. The authors ensure that they fall on the near side of difference for their readers by disavowing aspects of Asian cuisine that their readers might find challenging, particularly dog meat. These culinary disavowals extend into the human register when the authors highlight the otherness of the people they encounter in Asia.Ironically, these texts of cultural self-discovery demonstrate the longevity of the stereotype of the Asian American as inauthentic US citizen.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.008 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".