Safe Routes to School: Providing Better and Safer Transportation Choices for Students
Bibliographic record
Abstract
This study investigates the school commuting behaviour of children at three elementary schools in the District of North Vancouver, British Columbia. The intent of this research is to gain an understanding of the barriers to active commuting in these neighbourhoods and make design response recommendations that could be implemented by the District of North Vancouver and North Vancouver School District. The District of North Vancouver (DNV) and North Vancouver School District (NVSD) originally jointly conducted a school safety review and developed a Safe Routes to School plan for these three elementary schools. The purpose of this study was: to identify transportation issues and opportunities around each school; to develop recommendations to improve the safety and access to each school; and to promote healthy and active modes of transportation to school such as walking and cycling. Following the DNV and NVSD study, the topic was used as the basis for a Master's Degree Thesis, which used a mixed-method research approach that included the use of the survey data and site analysis. Distance was found to be the most significant barrier to active commuting for children at these schools. Other barriers found include traffic safety (intersections, speed and traffic volume), age of child, lack of adult supervision, before and after school activities and the condition of sidewalks. This study makes recommendations that are intended to help the District of North Vancouver and North Vancouver School District address the barriers to active commuting for the study area schools and prioritize sustainable transportation choices. (A) For the covering abstract of this conference see ITRD record number 201310RT334E.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".