‘Road safety is no accident’: building efficient road safety lead agencies, strategies and targets in the world, 2009–2023
Bibliographic record
Abstract
The objective of this study is to examine the pattern in building road safety lead agencies, national strategies and national quantified targets in the world in the period 2009-2023. This was done through an analysis of reported presence of road safety lead agencies, national strategies and national quantified targets based on data collected through a questionnaire for five global status reports on road safety. The results show that there has been a steady growth in road safety lead agencies, national strategies and quantified targets globally and by WHO regions in the period 2009-2023. While, on the one hand, substantial increases in these three governance features were observed in Africa and the Americas, on the other hand, slight declines were observed in these features in Europe and some countries in the Pacific. In conclusion, there has been a steady growth in the number of road safety lead agencies, national strategies and quaantified targets globally and by WHO regions in the period 2009-2023. However, there were declines in these governance tools in some countries. Further resarch into how efficiently these tools are being used is needed to provide insights into the effectiveness of road safety policies, organisations and institutions worldwide.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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 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".