Safe Journeys to School: A St. Albert Community Initiative
Bibliographic record
Abstract
In 2014, the City of St. Albert launched the Safe Journeys to School Initiative. This consisted of the formation of a Joint Public Steering Committee to oversee the review of traffic safety at all of its 26 exiting and two planned schools. The Steering Committee included representatio from the City, the four School Boards, the RCMP, and members of the general public. TranSafe Consulting was retained to conduct engagement of parents, students and the general public through a series of open houses, survey questionnaires, focus groups and on-site engagement tools. Engineering reviews were conducted at each school, covering the site, the street frontage and the surrounding neighbourhood, and observations were conducted at each school during the peak pick-up and drop-off periods. Collisions involving pedestrians, cyclists and school buses were analyzed. Research of best practices in Alberta and elsewhere was also conducted. This extensive research, observation and analysis led to the identification of opportunities for enhancements to City policies, programs, standards and practices, as well as specific engineering interventions at each school. Key opportunities included the acommodation of active travel modes, crosswalk safety, speed management, improved efficiency in pick-up/drop-off operations, effective management of winter conditions, and the implementation of student education emphasizing respectful behaviours.
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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 | 0.010 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.025 | 0.005 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 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".