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Record W1975033875 · doi:10.1002/jtr.591

An assessment of ‘international best practice’ in visitor attraction management: does Scotland really lag behind?

2007· article· en· W1975033875 on OpenAlexaboutno aff
Brian Garrod, Anna Leask, Alan Fyall

Bibliographic record

VenueInternational Journal of Tourism Research · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicCruise Tourism Development and Management
Canadian institutionsnot available
Fundersnot available
KeywordsVisitor patternBest practiceTourismMarketingPerceptionTest (biology)AdvertisingBusinessPublic relationsPolitical sciencePsychologyEconomicsManagementLaw

Abstract

fetched live from OpenAlex

Abstract This paper compares and contrasts selected management practices among visitor attractions in Scotland, Australia, Canada and New Zealand. The catalyst for the study was the growing perception that management practices among visitor attractions in Scotland are becoming increasingly outdated and that the sector needs to learn from ‘international best practice’ in this respect. A postal questionnaire was sent to all paid‐admission visitor attractions in the four countries. In total, 1022 visitor attractions replied, representing an overall response rate of 41%. Chi‐square analysis was then used to test various hypotheses relating to the uptake of these management practices. A key conclusion is that although management practices do vary significantly among the four countries, Scotland does not necessarily lag behind. Indeed, Scottish visitor attractions seem to lead the way in many respects. Meanwhile, the study finds no strong evidence to suggest that visitor attractions in the other three countries have indeed identified and are following a common ‘international best practice’. Copyright © 2007 John Wiley & Sons, Ltd.

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)0.016
metaresearch head score (Gemma)0.042
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 score0.066
Threshold uncertainty score0.131

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.042
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.004
Science and technology studies0.0020.004
Scholarly communication0.0040.002
Open science0.0010.004
Research integrity0.0000.001
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 head0.039
GPT teacher head0.506
Teacher spread0.467 · 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

Citations23
Published2007
Admission routes1
Has abstractyes

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