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Record W2169856284 · doi:10.1080/17450120600914522

Environmental equity is child's play: mapping public provision of recreation opportunities in urban neighbourhoods

2006· article· en· W2169856284 on OpenAlexaffabout
Jason Gilliland, Martin Holmes, Jennifer D. Irwin, Patricia Tucker

Bibliographic record

VenueVulnerable Children and Youth Studies · 2006
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsWestern University
Fundersnot available
KeywordsRecreationEquity (law)BusinessEnvironmental planningEconomic growthGeographyPolitical scienceEconomics

Abstract

fetched live from OpenAlex

Abstract This paper examines the spatial distribution of recreational opportunities for children and youth in a mid-sized Canadian city (London, Ontario), in relation to the socioeconomic status of neighbourhoods and estimated local need for publicly provided recreation spaces. Public recreation facilities (N = 537) throughout the city were identified, mapped and analysed in a geographic information system. To explore potential socio-environmental inequities, neighbourhoods (N = 22) were characterized by socioeconomic and environmental variables, an index of neighbourhood social distress, a neighbourhood play space needs index, and measures of the prevalence and density of recreational opportunities. The results of the spatial analysis indicate there is no systematic socioenvironmental inequity with respect to the prevalence and density of publicly provided neighbourhood recreation spaces; however, there are several areas in the city where youth do not have access to formal play spaces. We argue that to promote physical activity among urban children and youth, city planners and health policy analysts should consider carefully the geographical distribution of existing recreational opportunities and ensure that new publicly funded recreation spaces are provided to neighbourhoods with the greatest need. Further research should seek to identify what kinds of recreation spaces are most effective for promoting healthy behaviours among vulnerable children and youth.

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)0.001
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 score0.017
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.298
Teacher spread0.232 · 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

Citations64
Published2006
Admission routes2
Has abstractyes

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