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Record W4405179541 · doi:10.1080/14927713.2024.2433948

Making space and taking space: exploring the crowding experiences of people using wheeled mobility devices in parks

2024· article· en· W4405179541 on OpenAlexafffundvenue
Hannah Rose Dudney, Farhad Moghimehfar, Garrett A. Stone

Bibliographic record

VenueLeisure/Loisir · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsVancouver Island UniversityToronto Rehabilitation InstituteUniversity of Toronto
FundersSocial Sciences and Humanities Research Council of CanadaVancouver Island University
KeywordsCrowdingSpace (punctuation)CrowdsPhenomenology (philosophy)Public parkPublic spaceZoomPsychologyPhenomenonSociologyCrowding outSocial psychologyPublic relationsComputer scienceGeographyArchitectural engineeringEngineeringComputer securityPolitical scienceCognitive psychologyEnvironmental planning

Abstract

fetched live from OpenAlex

When parks and protected areas become busy, crowding concerns arise. Existing studies suggest that the negative aspects of crowding may not be experienced equally, particularly for those who may already encounter challenges navigating public spaces. This study utilized interpretive phenomenology to explore how people who use wheeled mobility devices (e.g. wheelchairs, scooters and adaptive bikes) experienced the phenomenon of park crowding. Seven women completed semi-structured interviews in parks of their choice or using Zoom video conferencing. Three themes related to navigating space in crowded parks were developed. Themes include taking up more space than ‘others,’ using your body and device to make space and navigating unpeopled space. This study concludes that crowding complicates social and physical navigation for participants and enhances awareness of their bodies in the spaces they occupy. Findings and practical recommendations shared in this study provide park planners and managers with new insights into the crowded park experience.

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 score0.074
Threshold uncertainty score0.999

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 studies0.0010.000
Scholarly communication0.0000.001
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 head0.091
GPT teacher head0.356
Teacher spread0.265 · 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
Published2024
Admission routes3
Has abstractyes

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