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Destination and En-Route Experiences among Train Travellers to Tibet

2009· article· en· W2029794459 on OpenAlexaff
Ming Su, Geoffrey Wall

Bibliographic record

VenueTourism Recreation Research · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicDiverse Aspects of Tourism Research
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsTourismDestination imageGeographyDestinationsAdvertisingMarketingSocioeconomicsBusinessSociology

Abstract

fetched live from OpenAlex

The Qinghai-Tibet railway opened in July 2006 and boosted tourism to Tibet markedly due to the increased accessibility and affordability of travel. This research explores the differences between pre-visit and post-visit travellers, in terms of their travel motivations, satisfaction with the train journey, and the destination experience of Tibet. Reasons to induce the pre- and post-visit differences are also discussed. The relative importance of the train journey in comparison with the destination experience in Tibet is also examined. A survey of 187 travellers, 82 for the pre-visit and 105 for the post-visit samples, was used to collect travellers' perceptions. It identified that travellers to Tibet are motivated in similar ways despite difference in their demographic and socio-economic backgrounds. Differences between pre-visit expectation and post-visit satisfaction towards different destination attributes are discussed. The importance of the railway to tourists' destination choice of Tibet and in their overall travel experience of Tibet is confirmed.

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)0.000
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.073
GPT teacher head0.443
Teacher spread0.370 · 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

Citations5
Published2009
Admission routes1
Has abstractyes

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