MétaCan
Menu
Back to cohort
Record W2972625033 · doi:10.1108/tr-05-2019-0163

The tourist experience life cycle: a perspective article

2019· article· en· W2972625033 on OpenAlexaff
Rachel Dodds

Bibliographic record

VenueTourism Review · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicDiverse Aspects of Tourism Research
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsTourismDestinationsOriginalityPerspective (graphical)MarketingValue (mathematics)Tourist destinationsFocus (optics)BusinessAdvertisingSociologyGeographyComputer scienceQualitative researchSocial science

Abstract

fetched live from OpenAlex

Purpose The purpose of this paper is to review the past literature of the tourist experience and propose a new model of behavior. Design/methodology/approach It is proposed that understanding of the visitors experience from a development standpoint may be useful in understanding how tourism destinations or indeed products may be experienced so differently. Therefore, a tourist experience life cycle has been put forth as a model. Findings This model may help to outline how tourists, like destinations, also go through a life cycle. The implications may help destinations better understand the different motivations of their visitors. Originality/value While the literature has discussed the tourism experience and how the customer experience has changed, there has been little focus on the longitudinal development of how the actual tourist experiences a destination.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.002
Scholarly communication0.0050.003
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.024
GPT teacher head0.369
Teacher spread0.345 · 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 designTheoretical or conceptual
Domainnot available
GenreCommentary

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

Citations17
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueTourism ReviewSame topicDiverse Aspects of Tourism ResearchFrench-language works237,207