A novel spatio-temporal examination of children's accessibility, exposure, and engagement to parks and recreation spaces in Middlesex-London, Ontario
Bibliographic record
Abstract
Children are spending more free time engaged in activities indoors, rather than in outdoor environments such as parks and recreation spaces. Parks and recreation spaces provide amenities that promote physical, cognitive, and social health among children. As it relates to the complexities of children’s living situations, properly measuring children’s levels of interactions with these spaces is poorly understood in geography research.\nBy examining various attributes of children, this thesis improves on the measurement of children’s levels of interactions with parks and recreation spaces. Research herein utilized household survey data, a high-resolution GIS dataset of environmental factors, and GPS logs from participants ages 9-14 years recruited throughout southwestern Ontario for a mixed-methods project conducted in 2010-2013.\nSociodemographic characteristics acquired from the survey and GPS tracks of the participants were linked to the GIS dataset, which included regional parks-and-recreation geospatial data. To compare measures for estimating levels of interactions with parks and recreation spaces, the merged dataset was examined through an Accessibility-Exposure-Engagement framework. Statistical tests revealed relationships between children’s home locations or sociodemographic characteristics, and levels of accessibility/exposure/engagement with specific parks and recreation amenities. Hierarchical regression modelling, with blocks containing sociodemographic variables, assessed the influence of individual, interpersonal, social, and built-environment characteristics on children’s proportion of free time in parks and recreation spaces.\nResults suggest measures of home location’s proximity to parks and recreation spaces do not represent frequency of exposure or duration of engagement to them by children. Child gender, visible minority status, and urbanicity are associated with proportion of free time in parks and recreation spaces. Moving forward, geography research should utilize the most accurate methods for estimating children’s levels of interactions with health-positive environments such as parks and recreation spaces.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".