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Record W7054965947

ASSESSING PUBLIC PARKS FOR CHILDREN IN LONDON, ONTARIO

2006· article· en· W7054965947 on OpenAlexaboutno aff

Bibliographic record

VenueScholarship@Western (Western University) · 2006
Typearticle
Languageen
FieldEngineering
TopicLaser Design and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsNeighbourhood (mathematics)CensusPublic parkSocioeconomic statusBuilt environmentGeographic information systemPsychological interventionPublic participation GISDistribution (mathematics)Urban planning
DOInot available

Abstract

fetched live from OpenAlex

Parks are an important feature of the local built environment. It is suggested that whether (or not) high quality public parks are available in the local environment has a significant influence on physical activity levels among youth. This study examines the location and quality of neighbourhood parks (n=208) in a mid-sized Canadian city (London, Ontario) to determine if these public facilities are adequately and equitably distributed throughout the city. A geographic information system was used to map and analyze the spatial distribution of public parks in urban and suburban neighbourhoods of varying socioeconomic characteristics. Neighbourhoods were differentiated using 2001 socio-economic data from the Census of Canada aggregated to City of London planning districts. Comprehensive field surveys were conducted at each park (n=208) in every non-rural district in order to assess quality. The preliminary findings of this study suggest that there is no systematic socio-spatial pattern of inequity with respect to park provision. Better information about neighbourhood play spaces is crucial in the struggle against childhood obesity as physical activity is one of the most important ways to curb obesity. With this information, planners and policy makers can begin to make interventions to make neighbourhoods healthier for children.

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)0.000
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: Empirical
Teacher disagreement score0.133
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
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 head0.277
Teacher spread0.211 · 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
Published2006
Admission routes1
Has abstractyes

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