Bibliographic record
Abstract
In an effort to reduce the likelihood and severity of crashes involving children, many jurisdictions have reduced the legal speed limit in school zones and some localities have also reduced the speed limit on roads around playgrounds. Although the two types of reduced speed zones should produce similar outcomes in terms of compliance and traffic speed, driver perception and acceptance of the two treatments may be different. This study measures traffic speeds and compliance rates in school and playground zones to determine if drivers behave differently in different types of zones. The study also examined the effects of road width (number of lanes) and the presence of fencing on traffic speeds and compliance rates. Spot speed measurements were collected at selected school and playground zones in Calgary, Alberta. The zones had a legal speed limit of 30 km/h and were located in residential areas that would otherwise have a limit of 50 km/h. Results showed that the mean speed in both the school and playground zones was slightly higher than the legal limit of 30 km/h but substantially lower than 50 km/h. The mean speed in playground zones was slightly but statistically significantly higher than the mean in school zones. Playground zones also had a higher noncompliance rate. Mean speeds and noncompliance were slightly higher on four-lane roads compared to two-lane roads. Mean speed and noncompliance rates were lower in zones with chain-link fencing.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".