Feeding a tourism boom: changing food practices and systems of provision in Hoi An, Vietnam
Bibliographic record
Abstract
While food studies have increasingly gone beyond the “Western” experience in food globalization processes, research on food and tourism has often prioritized the (Western) tourist’s gaze. In the literature on food and tourism in Asia, little attention has been given to the experiences of host populations. Responding to this lacuna in the literature, this paper analyses how a tourism boom is fed and how tourism-driven “foodway encounters” shape food practices and systems of provision. Focusing on the major tourism transformations seen in the UNESCO World Heritage Site of Hoi An, Vietnam, over the past decades, we study how hosts approach tourists’ demand for both comfort food from home and new food experiences that are simultaneously “authentic” and safe. We analyze how both Vietnamese and foreign hosts seek to understand, influence and adapt to the culinary preferences of visitors, and how they develop the necessary skills to do so. Furthermore, since feeding tourists often requires a wide range of food traditionally unavailable or uncommon in Hoi An, we analyze how hosts acquire the ingredients necessary for changing food practices and how systems of provision both shape and take shape through the process of catering to the particularities of touristy foodways.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".