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Record W4386148273 · doi:10.1080/11745398.2023.2250477

Supporting rural low-income families: a municipal recreation department's response to community crisis

2023· article· en· W4386148273 on OpenAlexafffundabout
Jackie Oncescu, Megan Fortune, Laura Fisher, Mary Sweatman, Julia Frigault

Bibliographic record

VenueAnnals of Leisure Research · 2023
Typearticle
Languageen
FieldPsychology
TopicRecreation, Leisure, Wilderness Management
Canadian institutionsAcadia UniversityUniversity of New Brunswick
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsRecreationEconomic growthWork (physics)Political scienceSocioeconomicsSociologyEconomicsLaw

Abstract

fetched live from OpenAlex

Recreation is an important resource that can support residents’ capacity to cope with stress and deal with community crisis, such as a pandemic. However, rural low-income families often experience inequitable access to recreation provisions. COVID-19 pandemic forced municipal recreation departments across Canada to re-evaluate and adapt their provisions, of particular importance for rural low-income families. Through the lens of social liberalism, this study examined the role of a municipal recreation department's response to community crisis and the implications of its provisions on rural low-income mothers and their families’ capacity to facilitate leisure during the pandemic. Through 29 interviews with low-income mothers and a focus group with the recreation department, we illuminate how provisions were designed and delivered to address income inequality, geographic isolation, social exclusion and childcare. Considering these findings, we discuss the department's approach to redesigning and delivering provisions and the implications to supporting low-income families’ access to recreation.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.083
Threshold uncertainty score0.165

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0090.002
Scholarly communication0.0020.001
Open science0.0020.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.229
GPT teacher head0.527
Teacher spread0.299 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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 routes3
Has abstractyes

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