Outdoor leisure with dogs: an empirical evaluation of visiting shared outdoor leisure spaces in the UK
Bibliographic record
Abstract
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. RÉSUMÉCette étude porte sur les visites d'espaces de loisirs partagés extérieurs (SOLS en anglais) avec des chiens, tels que des parcs, des forêts et des plages au Royaume-Uni.Des hypothèses et un modèle conceptuel ont été élaborés en fonction des données qualitatives et descriptives antérieures.Un sondage en ligne auprès des gardiens de chiens (n = 602) a été analysé à l'aide de la modélisation par équation structurelle des moindres carrés partiels (PLS-SEM) afin de vérifier les effets de la motivation intrinsèque de la personne, du bien-être du chien, de l'avantage pour la communauté et des liens sociaux sur les composantes de la participation aux loisirs (attraction, centralité et expression de soi) ainsi que sur l'intention et le comportement de visite par rapport aux SOLS.Les résultats ont montré que la motivation intrinsèque de la personne et l'avantage communautaire avaient une incidence positive sur
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".