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Record W4221132956 · doi:10.25035/visions.24.01.10

Mass – Market US – Caribbean Cruise Travel During the COVID-19 Pandemic: Implications for Culinary Tourism

2022· article· en· W4221132956 on OpenAlexaff
Shayan S. Lallani

Bibliographic record

VenueVisions in leisure and business · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicCruise Tourism Development and Management
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsCruiseCoronavirus disease 2019 (COVID-19)TourismPandemic2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)GeographyBusinessVirologyMedicineOceanographyOutbreakInternal medicineGeologyInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

Culturally themed dining is a popular offering on mass-market cruises operating in the US-Caribbean market, with companies often marketing these experiences as opportunities for immersion in societies perceived as foreign. The COVID-19 pandemic impacted every aspect of cruise ship dining. As an intimate activity that brings people together through the sharing of food and culinary memories, dining on cruise ships as it was pre-pandemic is no longer feasible during a global event that asks people to stay apart. Cruise lines and authorities responsible for regulating the industry have implemented important safety measures to protect passengers and crew onboard, as well as locals at destination cruise ports. This article argues that these same safety considerations obscure advertised representations of cultural authenticity in spaces of dining, reducing the possibility that guests will view cruise dining as opportunities for culinary tourismas immersion in the societies represented. In considering how COVID-19 restrictions and mandates have unintendedly impacted the production of culinary tourism on cruise ships and at ports of call, this work seeks to pave the way for future research on the pandemic's implications for cultural tourism.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.381
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0030.000
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 head0.046
GPT teacher head0.334
Teacher spread0.288 · 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.

Study designObservational
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

Citations0
Published2022
Admission routes1
Has abstractyes

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