Coordinated evaluation of active and sustainable school travel : international best practices
Bibliographic record
Abstract
More children across the Greater Toronto and Hamilton Area (GTHA) are driven to school than ever before, which is detrimental to their health and wellbeing, and contributes to traffic congestion and reduced environmental sustainability. Active and sustainable school travel (ASST) describes sustainable modes of school travel. The Big Move envisions that 60% of children will utilize ASST by 2033. However, contemporary data collection efforts are not coordinated across the region making it difficult to measure progress towards this goal. This paper explores international best practices for coordinated data collection and evaluation of school travel-related programming. Five recommendations are made for future school travel data collection efforts in the GTHA related to stakeholder relationship building, incentivizing data collection, utilizing multiple data collection tools, developing holistic performance indicators, and establishing clear leadership from one organization. Key Words: active transportation, evidence-based planning, data collection, evaluation, children, planning
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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 | 0.453 | 0.315 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.022 | 0.008 |
| Open science | 0.005 | 0.015 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".