Development of a Canadian Index of Playability
Bibliographic record
Abstract
This thesis describes the creation and testing of a Canadian index of the “playability” of neighbourhoods, defined as the degree to which neighbourhoods encourage unsupervised outdoor active play in children between the ages of 10 and 13. It consists of three manuscripts. The first manuscript describes the creation and testing of a novel measurement method for features of the built environment that have been associated with the propensity of children to engage in outdoor active play. Google Street View images were systematically downloaded from the area surrounding children’s homes and processed with a neural network to extract features of the built environment. The effectiveness of this method was measured against ground truth information. The second manuscript describes the initial development of a prediction model for time spent in unsupervised outdoor activity using a sample of children from three neighbourhoods in the Greater Vancouver Area. Predictor selection was based on qualitative interviews with children conducted by the Playability Project team at the University of British Columbia. A Bayesian model of play was constructed using prior probabilities informed by the things that children indicated were important for their engagement in play. The third manuscript tests and extends the model developed in manuscript two in a cross-Canadian population of children identified using the Health Behaviour in School-aged Children survey. The predictive validity of the initial model is compared against similar models trained in the HBSC data. The Playability score developed across these manuscripts shows a low correlation with play behaviour when tested outside of the training sample, but suggests avenues for future research into built environment predictors of the play behaviour of children. The score could be refined with larger-scale datasets with objective measurements of play, and future research could examine of the causal impact of these predictors using interventional trials or by taking advantage of natural experiments. An accurate index of Playability could be used to inform the development of neighbourhoods and cities that encourage outdoor active play in children, improving their health and well-being.
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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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".