MétaCan
Menu
Back to cohort
Record W2170009476 · doi:10.1177/0047287505274646

Destination Branding: Insights and Practices from Destination Management Organizations

2005· article· en· W2170009476 on OpenAlexaff
Carmen Blain, Stuart E. Levy, J. R. Brent Ritchie

Bibliographic record

VenueJournal of Travel Research · 2005
Typearticle
Languageen
FieldSocial Sciences
TopicDiverse Aspects of Tourism Research
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsTourismDestination managementMarketingBusinessCorporate brandingDestination marketingProduct (mathematics)DestinationsPhenomenonLogo (programming language)Brand managementPublic relationsPolitical scienceComputer science

Abstract

fetched live from OpenAlex

Although the concept of branding has been applied extensively to products and services, tourism destination branding is a relatively recent phenomenon. In particular, destination branding remains narrowly defined to many practitioners in destination management organizations (DMOs) and is not well represented in the tourism literature. Consequently, this study has three goals. First, it attempts to review the conceptual and theoretical underpinnings of branding as conveyed by leading authors in the marketing field. Second, it seeks to refine and enhance the definition of destination branding (acceptable to and understood by tourism destination managers) to more fully represent the complexities of the tourism product. Third, and most importantly, it seeks to improve our understanding of current destination branding practices among DMOs. The findings indicate that although DMO executives generally understand the concept of destination branding, respondents are implementing only selective aspects of this concept, particularly logo design and development.

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)0.005
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0050.003
Scholarly communication0.0060.004
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.113
GPT teacher head0.457
Teacher spread0.344 · 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 designQualitative
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

Citations957
Published2005
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Travel ResearchSame topicDiverse Aspects of Tourism ResearchFrench-language works237,207