Law Accommodating Nonmotorized Road Users and Pedestrian Fatalities in Florida, 1975 to 2013
Bibliographic record
Abstract
OBJECTIVES: To examine the effect of Florida's adoption of Statute 335.065-a law requiring the routine accommodation of nonmotorized road users (i.e., a "Complete Streets" policy)-on pedestrian fatalities and to identify factors influencing its implementation. METHODS: We used a multimethod design (interrupted time-series quasi-experiment and interviews) to calculate Florida's pedestrian fatality rates from 1975 to 2013-39 quarters before and 117 quarters after adoption of the law. Using statistical models, we compared Florida with regional and national comparison groups. Semistructured interviews were conducted with 10 current and former Florida transportation professionals in 2015. RESULTS: Florida's pedestrian fatality rates decreased significantly-by at least 0.500% more each quarter-after Statute 335.065 was adopted, resulting in more than 3500 lives saved across 29 years. Interviewees described supports and challenges associated with implementing the law. CONCLUSIONS: Florida Statute 335.065 is associated with a 3-decade decrease in pedestrian fatalities. The study also reveals factors that influenced the implementation and effectiveness of the law. Public Health Implications. Transportation policies-particularly Complete Streets policies-can have significant, quantifiable impacts on population health. Multimethod designs are valuable approaches to policy evaluations.
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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 | 0.002 | 0.008 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".