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Record W4417477099 · doi:10.1080/14927713.2025.2601519

Reclaiming recreation: leveraging a participatory mapping approach for effective recreational PA advocacy

2025· article· en· W4417477099 on OpenAlexafffundvenueabout
Madeleine D. Sheppard-Perkins, N. W. Simpson, Tomoko McGaughey, Lyndsay Hayhurst, Francine Darroch

Bibliographic record

VenueLeisure/Loisir · 2025
Typearticle
Languageen
FieldPsychology
TopicRecreation, Leisure, Wilderness Management
Canadian institutionsYork UniversityCarleton University
FundersSocial Sciences and Humanities Research Council of CanadaPublic Health Agency of Canada
KeywordsRecreationCitizen journalismKey (lock)Participatory action researchAgency (philosophy)Work (physics)Citizen science

Abstract

fetched live from OpenAlex

This brief report presents a feminist participatory mapping initiative addressing inequities in recreational physical activity (PA) access within a social housing community in [municipality], Canada, with a focus on methodological innovation and advocacy outcomes. Using an intersectional geography lens, the project conceptualized ‘recreational PA deserts’ as areas where environmental, social, and policy barriers restrict access to health-promoting spaces. Conducted with [community-based organization], the initiative combined community move-throughs, sketch mapping, interviews, data scans, and digital cartography to identify barriers and co-create an advocacy story map. Embedded within a broader evaluation of trauma- and violence-informed physical activity programmes, the project emphasized community leadership throughout. Key findings highlighted poor infrastructure, safety concerns, and transportation barriers as major obstacles despite strong community interest in recreational PA. Outcomes included new funding for intergenerational programming and municipal investment in infrastructure. This brief paper contributes to leisure studies by demonstrating how participatory mapping can be used to identify recreational PA barriers and mobilize community-led advocacy for infrastructure and policy change.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.695
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
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 head0.066
GPT teacher head0.347
Teacher spread0.281 · 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.

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 routes4
Has abstractyes

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