PW 0318 Child pedestrian risk and social equity: spatial distribution of roadway safety features in toronto, canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Investments in road design features are made to improve pedestrian safety in urban areas. Pedestrian motor vehicle collisions (PMVC); however, remain common, and occur at higher frequency in lower income neighborhoods. The objective of this study was to compare child PMVC rates and the distribution of roadway environment features related to child pedestrian safety in low versus high income clusters, in Toronto, Canada. Spatial cluster detection by census tract identified low and high income clusters using Canadian census data. Police-reported data of 2185 PMVCs involving children ages 5–14 from 2001–2010 were mapped with speed humps, crossing guards, missing sidewalks, one-way streets and local roads. Relationships between roadway features and low versus high income clusters were examined using multiple logistic regression. Of 524 census tracts, fifty eight (11%) were in high and 44 (8%) were in low income clusters. Collision rates were almost 6 times higher in low income clusters. For every km/10 km road increase of speed humps there was a 65% <i>lower</i> odds, for every km/10 km road increase in local roads there was a 38% <i>lower</i> odds and for every additional crossing guard/10 km road there was a 43% <i>greater</i> odds of being in a lower income cluster. Fewer lower speed local roads and speed humps in lower income areas may put children at increased risk. The inequity in spatial distribution of speed humps may due to process of request which is initiated by the community and may favour higher income communities. More school crossing guards may reflect more children walking in lower income areas, as well as attempts to ameliorate more dangerous road environments with higher PMVC rates. Policy implications relate to the equitable distribution of roadway features to provide safe pedestrian environments.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it