Assessing the Risk of Road Traffic Fatalities Across Sub-Populations of a Given Geographical Zone, Using a Modified Smeed’s Model
Bibliographic record
Abstract
Smeed (1949) provided a regression model for estimating road traffic fatalities (RTFs). In this paper, a modified form of Smeed’s (1949) model is proposed for which it is shown that the multiplicative error term is less than that of Smeed’s original model for most situations. Based on this Modified Smeed’s model, Bayesian and multilevel methods are developed to assess the risk of road traffic fatalities across sub populations of a given geographical zone. These methods consider the parameters of the Smeed’s model to be random variables and therefore make it possible to compute variances across space provided there is significant intercept variation of the regression equation across such regions. Using data from Ghana, the robustness of the Bayesian estimates was indicated at low sample sizes with respect to the Normal, Laplace and Cauchy prior distributions. Thus the Bayesian and Multilevel methods performed at least as well as the traditional method of estimating parameters and beyond this were able to assess risk differences through variability of these parameters in space.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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 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".