Road safety in Latin America and the Caribbean: after a decade of action prospects for safer mobility
Bibliographic record
Abstract
The first Decade of Action for Road Safety has ended, but not without demonstrating the importance that systemic work on road safety offers towards protecting life. At the same time, it is clear that real progress in road safety requires a significant commitment of time, resources and political effort. In the Latin American and Caribbean region (LAC), over the past ten years, the growth in the rate of vehicle deaths has slowed. Unfortunately, in the last two years this trend has reversed due to the expansion of the motorcycle fleet in the region. Although it has been possible to raise awareness, create institutions, plan and implement important actions to reduce fatalities, LAC has not obtained the same results as high-income countries, which have entirely reversed the trend in road deaths. In accordance with the above, it is possible to conceive of a second decade of action as an opportunity for LAC countries to finalize the processes initiated over the past decade and to introduce successful lessons experiences from other countries in the region. With this study, the IDB intends to guide this process of continuous improvement, highlighting the best practices and offering an overview of how to move from theory to practice, following the principles that mobility must be safe, sustainable and inclusive, while reducing the risk to all road users, especially the most vulnerable, and maintain the focus on users with special needs (people with disabilities, children and the elderly).
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".