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Exploring the relationship between tourism acceptance and perceived quality of life in big German cities: the moderating role of tourism intensity

2025· article· en· W6941076914 on OpenAlexaff

Bibliographic record

VenueBreda University of Applied Sciences Portal · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMycorrhizal Fungi and Plant Interactions
Canadian institutionsImpact
Fundersnot available
KeywordsTourismGermanQuality (philosophy)Empirical researchQuality of life (healthcare)Perceived qualityTourism geography

Abstract

fetched live from OpenAlex

Leisure travelling is known to be a contributor to visitors’ well-being and quality of life yet only little is known about the influence of tourism on the destination residents’ quality of life. Given rising imbalances and perceived conflicts of use between visitors and residents particularly in an urban context, research interest in residents’ perspectives have increased and new measures such as the tourism acceptance score have been developed to measure perceived tourism impacts over time. While tourism intensity has been proposed to be an indicator for low tourism acceptance and decreasing quality of life, little empirical evidence is existing. This study examines the relationship between tourism acceptance and perceived quality of life and the moderating role of tourism intensity. The data base used consisted of representative samples in 11 German cities. Results demonstrate a positive relationship between personal tourism acceptance and the residents’ quality of live. This relationship is moderated by the tourism intensity and is generally stronger in cities with higher tourism intensity.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.031
Threshold uncertainty score0.691

Codex and Gemma teacher scores by category

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

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 head0.076
GPT teacher head0.246
Teacher spread0.171 · 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 teacher head, 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

Citations0
Published2025
Admission routes1
Has abstractyes

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