MétaCan
Menu
Back to cohort
Record W2962965537 · doi:10.5539/ibr.v12n8p23

Information Sources and Domestic Tourism at Sanyuesan Festival, China

2019· article· en· W2962965537 on OpenAlexvenueno aff
Qin Xi, Ousanee Sawagvudcharee, John Walsh

Bibliographic record

VenueInternational Business Research · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicSport and Mega-Event Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsTourismSample (material)ChinaDistribution (mathematics)MarketingBusinessPhenomenonPopulationDomestic tourismGeographyAdvertisingSociologyTourism geographyMathematics

Abstract

fetched live from OpenAlex

Festival tourism is part of a phenomenon which is rather larger than its tourism component. The relationship between people and the various elements of the festival may be complicated by the changing availability of information sources, including web-based sources, as well as changing relationships towards those sources with respect to access ease and trust. A sample of 400 respondents was achieved via online questionnaire distribution to investigate these issues and to formulate lessons that might inform marketing strategies relating to future iterations of the festival. It was found that no straightforward relationships existed with respect to segmentation of the sample and, therefore, population and that attitudes towards both information sources and the institutions of the festival are undergoing a process of change.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.040
Threshold uncertainty score0.080

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.041
GPT teacher head0.400
Teacher spread0.359 · 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

Citations4
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueInternational Business ResearchSame topicSport and Mega-Event ImpactsFrench-language works237,207