Bibliographic record
Abstract
Countries with high cycling rates have national, school-based, mandatory cycling education programs; however, in North America, cycling education is diverse and disparate. The aim of this project was to understand what cycling safety content was delivered in Canadian jurisdictions and how training materials aligned with scientific evidence. Cycling safety literature was reviewed, and cycling education materials were compiled from drivers' licensing and cyclist education programs. The education materials were compared with the scientific evidence found in cycling safety literature to determine agreements, disagreements, or gaps. Fifty-six scientific articles focused on crash or injury risk, injury severity, or other safety outcomes and met the project's inclusion criteria. The evidence in these articles covered bicycling operations, visibility and safety gear, road characteristics, route types, and bicycle–motor vehicle interactions. Forty-eight training materials for cyclists, drivers, or both were gathered from 12 provincial and territorial driver's licensing jurisdictions, five municipalities, and seven advocacy organizations. Materials covered bicycle fit and maintenance, rules of the road, bicycle operations, visibility and safety gear, bicycle–motor vehicle interactions, route characteristics, and route types. Most education topics were supported by scientific evidence, except topics related to legislation or common sense. Evidence on motor vehicle passing distances conflicted with some educational material guidance about where to cycle on the road. A gap in the educational materials was the relative safety of different route infrastructure, important for route planning. This research illustrated the diversity of cycling education in Canada and revealed areas in which education materials could be modified to align with scientific evidence on safe cycling.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.204 | 0.575 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.017 | 0.015 |
| Science and technology studies | 0.005 | 0.019 |
| Scholarly communication | 0.039 | 0.032 |
| Open science | 0.007 | 0.013 |
| Research integrity | 0.018 | 0.018 |
| Insufficient payload (model declined to judge) | 0.028 | 0.007 |
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".