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Beating the Pandemic, One Bite at a Time: How the Gastronomy Sector in British Columbia is Forging Its Own Path Forward

2021· article· en· W3205469809 on OpenAlexaffabout
Nazmi Kamal, Marian Chung

Bibliographic record

VenueGastronomy and Tourism · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicSupply Chain Resilience and Risk Management
Canadian institutionsCapilano University
Fundersnot available
KeywordsDiversification (marketing strategy)PandemicLoyaltyRevenueGastronomyMarketingPsychological resilienceCoronavirus disease 2019 (COVID-19)Resilience (materials science)Qualitative researchQualitative propertyPublic relationsSociologyPolitical scienceBusinessSocial sciencePsychologyTourismMedicineSocial psychologyComputer scienceLaw

Abstract

fetched live from OpenAlex

This research was conducted during the second COVID-19 lockdown in British Columbia, Canada. Its aim was to reveal the opportunities that emerged for gastronomic experience providers which enabled them to build economic, social, and or environmental resilience during the pandemic. Using an interpretative, qualitative approach and case study methodology, data were gathered through semistructured interviews. Nineteen responses were collected and reflected the following key findings. First, technology was a primary tool used in paving the way for strategic and operational changes. Second, expansion into retail as a revenue diversification tool is key to creating sustained economic growth. Finally, the sense of community is at an all-time peak as shown by collaborative spirit, customer loyalty, and philanthropic initiatives across the sector. The findings also suggest a postpandemic gastronomic scene in British Columbia that is heavily supported by domestic palates, a diversified offering, and pandemic-proof experiences.

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 categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.489
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
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 head0.010
GPT teacher head0.180
Teacher spread0.170 · 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
Published2021
Admission routes2
Has abstractyes

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