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Record W4280512997 · doi:10.5539/ibr.v15n6p65

Changes in Consumption Habits in Restaurant Diners before and during the COVID-19 Pandemic, in Cancun, Quintana Roo

2022· article· en· W4280512997 on OpenAlexvenueno aff
Mauro Felipe Berumen Calderón, Damayanti Estolano Cristerna, Sandra Guerra Mondragón, Angélica Selene Sterling Zozoaga

Bibliographic record

VenueInternational Business Research · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCOVID-19 Pandemic Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsConsumption (sociology)PandemicPopulationCoronavirus disease 2019 (COVID-19)Food consumptionFood habitsGeographyEnvironmental healthDemographyBiologySocioeconomicsEconomicsDiseaseAgricultural economicsMedicineSociology

Abstract

fetched live from OpenAlex

Diverse factors can influence consumers' purchase intention leading them to change their consumption habits. The COVID-19 disease has influenced the population's behavior patterns, lifestyle changes, and food consumption impacting the restaurant sector. This research is a non-experimental, cross-sectional with a quantitative approach. The correlational scope proved the association and variability referring to the consumption habits of diners in Cancun, Quintana Roo, showing the changes that defined this population before and during the pandemic. Significant variations showed up: the average group size for eating out during labor days leads to an estimate of a decrease of 44.1% in weekly income for restaurants derived from group behaviors. In their habits with friends and family, this same variable decreased by 56%.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.141
Threshold uncertainty score0.281

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.146
GPT teacher head0.377
Teacher spread0.230 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations1
Published2022
Admission routes1
Has abstractyes

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