Setting Road Safety Targets in Cambodia: A Methodological Demonstration Using the Latent Risk Time Series Model
Why this work is in the frame
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Bibliographic record
Abstract
We present the methodology used for estimating forecasts for the number of road traffic fatalities in 2011–2020 in Cambodia based on observed developments in Cambodian road traffic fatalities and motor vehicle ownership in the years 1995–2009. Using the latent risk time series model baseline forecasts for the fatality risk were estimated for the years 2010–2020. These baseline forecasts were then used to obtain estimates for the future number of fatalities based on three scenarios for the future Cambodian growth in motor vehicle ownership: a low, a middle, and a high growth scenario. The middle growth scenario results in an expected death toll of approximately 3,200 in 2020. In 2010, it was therefore decided in Cambodia to set the target at a 50% reduction of this number or 1,600 fatalities in 2020. If it is possible to achieve this target by taking additional actions to improve road safety, then a total of 7,350 lives could be saved in Cambodia over the whole 2011–2020 period.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it