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Record W2056262189 · doi:10.1177/1356766714542189

Understanding supply- and demand-side destination image relationships

2014· article· en· W2056262189 on OpenAlexaff
Shaojun Ji, Geoffrey Wall

Bibliographic record

VenueJournal Of Vacation Marketing · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicDiverse Aspects of Tourism Research
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsMarketingPublicityPromotion (chess)AdvertisingGovernment (linguistics)Quality (philosophy)BusinessDestination imageChinaAffect (linguistics)TourismDestinationsGeographyPsychologyPolitical science

Abstract

fetched live from OpenAlex

Image promotion has been a primary focus of destination marketing. Marketers are eager to learn whether their projected images have been transmitted to potential consumers and are incorporated into their images of the destination. This research compares the images projected by Qingdao (China) government agencies and those perceived by current visitors. The projected and perceived images of Qingdao were investigated by analyzing publicity materials and a questionnaire survey of visitors, respectively. Their relationships were explored using both a qualitative evaluation and statistical tests. The results show that the two types of images incorporated certain common image themes. However, the emphasis of the projected images did not relate significantly to what visitors considered to affect the quality of their experience. Therefore, Qingdao’s projected image was only partially delivered to and embraced by current visitors.

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)0.003
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 score0.008
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0040.005
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.000

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 head0.117
GPT teacher head0.342
Teacher spread0.225 · 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

Citations35
Published2014
Admission routes1
Has abstractyes

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