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Record W2072909380 · doi:10.1108/09596110810866073

Tourism in Niagara: identifying challenges and finding solutions

2008· article· en· W2072909380 on OpenAlexaffabout
Chandana Jayawardena

Bibliographic record

VenueInternational Journal of Contemporary Hospitality Management · 2008
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicWine Industry and Tourism
Canadian institutionsGeorge Brown College
Fundersnot available
KeywordsTourismTheme (computing)OriginalityHospitalityContext (archaeology)NarrativeValue (mathematics)Perspective (graphical)SociologyHeritage tourismFoundation (evidence)MarketingManagementPublic relationsTourism geographyHistorySocial sciencePolitical scienceQualitative researchComputer scienceEconomicsBusinessArchaeologyWorld Wide WebArt

Abstract

fetched live from OpenAlex

Purpose The purpose of this paper is to provide a relevant backdrop for the Worldwide Hospitality snd Tourism Themes (WHATT) theme issue on tourism challenges and solution in the Niagara region, and to present the key points discussed during the 2007 WHATT roundtable discussion in the Niagara region, Canada. Design/methodology/approach The approach of this paper is more a narrative one. It also draws data from a series of web sites to analyse the past and present performance of tourism. Findings This paper provides a historic perspective of the Niagara region and presented in the context of tourism in the world, Americas, Canada, and Ontario. Then it travels back to explain the origins of WHATT and its scholarly journey over the years. In capturing the essence of the 2007 WHATT roundtable discussion in Niagara, the paper provides a strong foundation for the other nine articles, which follow in this WHATT theme issue. Originality/value In a world of theories, this paper provides fresh perspectives on many relevant ideas by using original expert views. Readers who are interested in the Niagara region would benefit from this paper.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.534
Threshold uncertainty score0.616

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
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 head0.087
GPT teacher head0.277
Teacher spread0.189 · 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.

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

Citations8
Published2008
Admission routes2
Has abstractyes

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