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Record W2731289286 · doi:10.1108/whatt-05-2017-0024

State of the Canadian hospitality and tourism industry

2017· article· en· W2731289286 on OpenAlexaffabout
Chandana Jayawardena, Altaf Sovani, Alanna MacDonald

Bibliographic record

VenueWorldwide Hospitality and Tourism Themes · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal socioeconomic and cultural dynamics
Canadian institutionsAlgonquin College
Fundersnot available
KeywordsTourismHospitalityHospitality industryOriginalityHospitality management studiesEconomic shortageMarketingTheme (computing)Tourism geographyPublic relationsBusinessPolitical scienceManagementSociologyEconomicsGovernment (linguistics)Qualitative researchSocial science

Abstract

fetched live from OpenAlex

Purpose This paper aims to provide a backdrop to the Worldwide Hospitality Themes ( WHATT ) theme issue (volume 9, issue 4) on aspects of the hospitality and tourism industry of Canada. Design/methodology/approach Canadian hospitality and tourism educators and their counterparts in the industry have collaborated periodically to discuss the challenges they face and to find practical solutions. Outcomes of ten key initiatives in Canada during the past 15 years that brought leaders of the hospitality and tourism industry and academia together to create 50 academic papers are summarized. Findings This paper provides key information on Canada, its people, its economic conditions and the challenges of the five sectors of the tourism industry in Canada. By introducing the main challenges faced by each sector, this paper provides a foundation for the other articles that follow in this WHATT theme issue. Practical implications Canadian tourism is losing ground, tourism marketing budgets are significantly reducing and there is a labour shortage crisis which are identified as key challenges requiring urgent attention. In conclusion, the authors suggest practical solutions. Originality/value Readers who are interested in the Canadian hospitality and tourism industry will find this paper to be of interest.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.063
Threshold uncertainty score0.999

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 studies0.0030.002
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.012
GPT teacher head0.261
Teacher spread0.249 · 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

Citations2
Published2017
Admission routes2
Has abstractyes

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