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Record W4387879312 · doi:10.1080/15528014.2023.2263986

Feeding a tourism boom: changing food practices and systems of provision in Hoi An, Vietnam

2023· article· en· W4387879312 on OpenAlexaff
Arve Hansen, Outi Pitkänen, Binh N. Nguyen

Bibliographic record

VenueFood Culture & Society · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCulinary Culture and Tourism
Canadian institutionsMcGill University
Fundersnot available
KeywordsBoomTourismBusinessAgricultural economicsPolitical scienceGeographyEconomicsEngineeringArchaeologyEnvironmental engineering

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.838
Threshold uncertainty score0.310

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.040
GPT teacher head0.264
Teacher spread0.224 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations10
Published2023
Admission routes1
Has abstractyes

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