MétaCan
Menu
Back to cohort

Envisioning a Sustainable, Resilient Tourism Future

2025· article· en· W4408307953 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueTourism Analysis · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicDiverse Aspects of Tourism Research
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsTourismSustainabilityCoronavirus disease 2019 (COVID-19)WorkforcePublic relationsThematic analysisProduct (mathematics)BusinessSustainable tourismPandemicTourism geographySustainable developmentMarketingPolitical scienceSociologyQualitative researchSocial science

Abstract

fetched live from OpenAlex

Amid the COVID-19 pandemic, many studies have explored the impact and implications of this global crisis on tourism. This article, however, takes a unique approach by conducting a thematic review of more than 300 studies published on the Web of Science database during the pandemic, generating 12 topic clusters. With a strong focus on sustainability, the study’s objective is to encapsulate and present the responsibilities of tourism professionals, the desired behavior of tourists and host communities, and the research agenda for a more regenerative, resilient future for tourism. The overarching message that emerges from this in-depth analysis is one of change. The findings challenge all stakeholders to prioritize traveler safety, value the workforce, invest in technology, and adopt inclusive and sustainable approaches to product development based on lessons learned during the pandemic. Crucially, the article underscores the need for collaboration among all stakeholders, as it is only through collective action that a new, resilient path forward for tourism can be forged.

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.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.602
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.006
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.007
GPT teacher head0.319
Teacher spread0.312 · 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