Do Election Laws Restricting Public Road Publicity Reduce Road Traffic Crashes and Their Consequences?
Bibliographic record
Abstract
BACKGROUND: In April 2016, Chile enacted the Law 20,900, which restricted electoral publicity on public roads. It established two important regulations: first, candidates were allowed, 30 days before any election, to publicize their campaigns in specific street locations. Second, roadside publicity must follow strict size standards to avoid visual contamination. This article examines the impact of this regulation in reducing road traffic crashes. METHODS: We obtained a number of traffic injuries and fatalities per population from public records. A time-series difference-in-difference study, using generalized linear models with an interaction between time-period and intervention, compared a municipal election period before the introduction of Law 20,900 (2012) to the first municipal election affected by the law (2016). We adjusted for precipitation and temperature, and applied models to three cities: Santiago, Gran-Valparaíso, and Concepción. We assessed the overall impact of the intervention using random effects meta-analyses. RESULTS: The law was associated with a decrease of 0.01 (95% confidence interval [CI]: -0.02, -0.00) in Santiago, a decrease of 0.01 (95% CI: -0.03, -0.00) in Valparaíso and an increase of 0.09 (95% CI: 0.06, 0.13) in Concepción, in all daily injuries and fatalities per 100,000 population. After 40 days of its implementation, the intervention was associated with a mild absolute reduction of 34 (95% reduction interval: -270, 67) traffic injuries and fatalities. CONCLUSIONS: This study estimates that the regulation of public road publicity had an overall mild effect on reducing traffic injuries and fatalities in three large cities in Chile.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".