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Record W4389881046 · doi:10.1080/14927713.2023.2291017

Outdoor leisure with dogs: an empirical evaluation of visiting shared outdoor leisure spaces in the UK

2023· article· en· W4389881046 on OpenAlexvenueno aff
Lori S. Hoy, Brigitte Stangl, Nigel Morgan

Bibliographic record

VenueLeisure/Loisir · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHuman-Animal Interaction Studies
Canadian institutionsnot available
Fundersnot available
KeywordsCentralityStructural equation modelingPsychologySocial psychologyQuestionnaireRecreationAttractionTest (biology)Applied psychologySociologyEcologyMathematics

Abstract

fetched live from OpenAlex

This study examined visits to shared outdoor leisure spaces (SOLS) with dogs, such as parks, woodlands, and beaches in the UK. Based on past qualitative and descriptive data, hypotheses and a conceptual model were developed. An online survey of dog guardians (n = 602) was analyzed using partial least square structural equation modeling (PLS-SEM) to test the impacts of human intrinsic motivation; dog well-being; the community benefit; and social bonding on the components of leisure involvement (attraction, centrality, and self-expression), and subsequently intention and visiting behavior in relation to SOLS. The results showed that human intrinsic motivation and community benefit had a positive impact on all aspects of leisure involvement, while dog wellbeing only affected attraction, and social bonding impacted centrality and self-expression. These findings contribute to a better understanding of dog guardians' behavior of visiting SOLS in the UK, providing insights for stakeholders responsible for designing, managing, and promoting these spaces.

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)0.002
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 score0.131
Threshold uncertainty score0.869

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.070
GPT teacher head0.408
Teacher spread0.337 · 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

Citations3
Published2023
Admission routes1
Has abstractyes

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